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<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The information discovered by Unit 42 and shared here indicates Scarlet Mimic is likely a well-funded and skillfully resourced cyber adversary .' [/INST] [The:O, information:O, discovered:O, by:O, Unit:B-SecTeam, 42:I-SecTeam, and:O, shared:O, here:O, indicates:O, Scarlet:B-HackOrg, Mimic:I-HackOrg, is:O, likely:O, a:O, well-funded:O, and:O, skillfully:O, resourced:O, cyber:O, adversary:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Scarlet Mimic has carried out attacks using both spear-phishing and watering holes since at least 2009 with increasingly advanced malware , and has deployed malware to attack multiple operating systems and platforms .' [/INST] [Scarlet:B-HackOrg, Mimic:I-HackOrg, has:O, carried:O, out:O, attacks:B-OffAct, using:O, both:O, spear-phishing:B-Way, and:O, watering:B-Way, holes:I-Way, since:O, at:O, least:O, 2009:B-Time, with:O, increasingly:O, advanced:O, malware:O, ,:O, and:O, has:O, deployed:O, malware:O, to:O, attack:O, multiple:O, operating:O, systems:O, and:O, platforms:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This time I'm going to focus on malicious CHM files used by Silence APT .' [/INST] [This:O, time:O, I'm:O, going:O, to:O, focus:O, on:O, malicious:O, CHM:B-Tool, files:I-Tool, used:O, by:O, Silence:B-HackOrg, APT:I-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'If you haven't heard about it for some reason , I would recommend to read this detailed report by Group-IB , as this APT attacks not only Russian banks , but also banks in more than 25 countries .' [/INST] [If:O, you:O, haven't:O, heard:O, about:O, it:O, for:O, some:O, reason:O, ,:O, I:O, would:O, recommend:O, to:O, read:O, this:O, detailed:O, report:O, by:O, Group-IB:B-SecTeam, ,:O, as:O, this:O, APT:B-OffAct, attacks:I-OffAct, not:O, only:O, Russian:B-Area, banks:B-Idus, ,:O, but:O, also:O, banks:B-Idus, in:O, more:O, than:O, 25:O, countries:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The group primarily deploys spear-phishing e-mails to infect its targets , but was also responsible for a watering hole attack in 2013 .' [/INST] [The:O, group:B-HackOrg, primarily:O, deploys:O, spear-phishing:B-Way, e-mails:I-Way, to:O, infect:O, its:O, targets:O, ,:O, but:O, was:O, also:O, responsible:O, for:O, a:O, watering:B-OffAct, hole:I-OffAct, attack:I-OffAct, in:O, 2013:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The group uses legitimate administration tools to fly under the radar in their post-exploitation phase , which makes detection of malicious activity , as well as attribution more complicated .' [/INST] [The:O, group:B-HackOrg, uses:O, legitimate:B-Tool, administration:I-Tool, tools:I-Tool, to:O, fly:B-Purp, under:I-Purp, the:I-Purp, radar:I-Purp, in:O, their:O, post-exploitation:O, phase:O, ,:O, which:O, makes:O, detection:O, of:O, malicious:B-OffAct, activity:I-OffAct, ,:O, as:O, well:O, as:O, attribution:O, more:O, complicated:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'On January 12 , 2016 , Cylance published a blog linking an exploit document to the group Mandiant refers to as APT2 and CrowdStrike as , Putter Panda , .' [/INST] [On:O, January:B-Time, 12:I-Time, ,:I-Time, 2016:I-Time, ,:O, Cylance:B-SecTeam, published:O, a:O, blog:O, linking:O, an:O, exploit:O, document:O, to:O, the:O, group:O, Mandiant:B-SecTeam, refers:O, to:O, as:O, APT2:B-HackOrg, and:O, CrowdStrike:B-SecTeam, as:O, ,:O, Putter:B-HackOrg, Panda:I-HackOrg, ,:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In 2016 , Unit 42 launched an unprecedented analytic effort focused on developing a modern assessment of the size , scope and complexity of this threat .' [/INST] [In:O, 2016:B-Time, ,:O, Unit:B-SecTeam, 42:I-SecTeam, launched:O, an:O, unprecedented:O, analytic:O, effort:O, focused:O, on:O, developing:O, a:O, modern:O, assessment:O, of:O, the:O, size:O, ,:O, scope:O, and:O, complexity:O, of:O, this:O, threat:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In 2014 , Unit 42 released a report titled , 419 Evolution , that documented one of the first known cases of Nigerian cybercriminals using malware for financial gain .' [/INST] [In:O, 2014:B-Time, ,:O, Unit:B-SecTeam, 42:I-SecTeam, released:O, a:O, report:O, titled:O, ,:O, 419:O, Evolution:O, ,:O, that:O, documented:O, one:O, of:O, the:O, first:O, known:O, cases:O, of:O, Nigerian:B-Area, cybercriminals:B-HackOrg, using:O, malware:O, for:O, financial:B-Purp, gain:I-Purp, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'A few months later , in February 2017 , the FBI published a press release revising its estimates and stating that , Since January 2015 , there has been a 1,300 percent increase in identified exposed losses , now totaling over $3 billion , Recognizing the significance of this threat group , Unit 42 continues to track the evolution of Nigerian cybercrime under the code name SilverTerrier .' [/INST] [A:O, few:O, months:O, later:O, ,:O, in:O, February:B-Time, 2017:I-Time, ,:O, the:O, FBI:B-SecTeam, published:O, a:O, press:O, release:O, revising:O, its:O, estimates:O, and:O, stating:O, that:O, ,:O, Since:O, January:B-Time, 2015:I-Time, ,:O, there:O, has:O, been:O, a:O, 1,300:O, percent:O, increase:O, in:O, identified:O, exposed:O, losses:O, ,:O, now:O, totaling:O, over:O, $3:O, billion:O, ,:O, Recognizing:O, the:O, significance:O, of:O, this:O, threat:B-HackOrg, group:I-HackOrg, ,:O, Unit:B-SecTeam, 42:I-SecTeam, continues:O, to:O, track:O, the:O, evolution:O, of:O, Nigerian:B-Area, cybercrime:O, under:O, the:O, code:O, name:O, SilverTerrier:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the 2016 Internet Crime Report published by the FBI , BEC was specifically highlighted as a , Hot Topic , , having been attributed to more than US$360 million in losses and gaining status as its own category of attack .' [/INST] [In:O, the:O, 2016:B-Time, Internet:O, Crime:O, Report:O, published:O, by:O, the:O, FBI:B-SecTeam, ,:O, BEC:O, was:O, specifically:O, highlighted:O, as:O, a:O, ,:O, Hot:O, Topic:O, ,:O, ,:O, having:O, been:O, attributed:O, to:O, more:O, than:O, US$360:O, million:O, in:O, losses:O, and:O, gaining:O, status:O, as:O, its:O, own:O, category:O, of:O, attack:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Recognizing the significance of this threat group , Unit 42 continues to track the evolution of Nigerian cybercrime under the code name SilverTerrier .' [/INST] [Recognizing:O, the:O, significance:O, of:O, this:O, threat:B-HackOrg, group:I-HackOrg, ,:O, Unit:B-SecTeam, 42:I-SecTeam, continues:O, to:O, track:O, the:O, evolution:O, of:O, Nigerian:B-Area, cybercrime:O, under:O, the:O, code:O, name:O, SilverTerrier:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Pony is a fairly common malware family that has existed in various forms since 2012 , with our first indications of Nigerian use occurring in August 2014 .' [/INST] [Pony:B-Tool, is:O, a:O, fairly:O, common:O, malware:O, family:O, that:O, has:O, existed:O, in:O, various:O, forms:O, since:O, 2012:B-Time, ,:O, with:O, our:O, first:O, indications:O, of:O, Nigerian:B-Area, use:O, occurring:O, in:O, August:B-Time, 2014:I-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Of the four , KeyBase stands out due to its rapid rise in popularity , with a peak deployment of 160 samples per month and usage by 46 separate SilverTerrier actors , followed by a fairly rapid decline .' [/INST] [Of:O, the:O, four:O, ,:O, KeyBase:B-Tool, stands:O, out:O, due:O, to:O, its:O, rapid:O, rise:O, in:O, popularity:O, ,:O, with:O, a:O, peak:O, deployment:O, of:O, 160:O, samples:O, per:O, month:O, and:O, usage:O, by:O, 46:O, separate:O, SilverTerrier:B-HackOrg, actors:I-HackOrg, ,:O, followed:O, by:O, a:O, fairly:O, rapid:O, decline:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'NetWire , DarkComet , NanoCore , LuminosityLink , Remcos and Imminent Monitor are all designed to provide remote access to compromised systems .' [/INST] [NetWire:B-Tool, ,:O, DarkComet:B-Tool, ,:O, NanoCore:B-Tool, ,:O, LuminosityLink:B-Tool, ,:O, Remcos:B-Tool, and:O, Imminent:B-Tool, Monitor:I-Tool, are:O, all:O, designed:O, to:O, provide:B-Features, remote:I-Features, access:I-Features, to:O, compromised:O, systems:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Unit 42 analyzed the use of these six malware families and found that Nigerian actors are currently producing an average of 146 unique samples of malware per month ( see Figure 6 ) .' [/INST] [Unit:B-SecTeam, 42:I-SecTeam, analyzed:O, the:O, use:O, of:O, these:O, six:O, malware:O, families:O, and:O, found:O, that:O, Nigerian:B-Area, actors:B-HackOrg, are:O, currently:O, producing:O, an:O, average:O, of:O, 146:O, unique:O, samples:O, of:O, malware:O, per:O, month:O, (:O, see:O, Figure:O, 6:O, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Given this requirement , SilverTerrier actors often rely on Dynamic DNS and virtual private servers to provide a layer of obfuscation to protect their identities .' [/INST] [Given:O, this:O, requirement:O, ,:O, SilverTerrier:B-HackOrg, actors:I-HackOrg, often:O, rely:O, on:O, Dynamic:B-Tool, DNS:I-Tool, and:O, virtual:B-Tool, private:I-Tool, servers:I-Tool, to:O, provide:O, a:O, layer:O, of:O, obfuscation:O, to:O, protect:O, their:O, identities:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'When using email scams , SilverTerrier actors preferred to use large target audiences , which maximized the likelihood of success with very little risk .' [/INST] [When:O, using:O, email:B-Way, scams:I-Way, ,:O, SilverTerrier:B-HackOrg, actors:I-HackOrg, preferred:O, to:O, use:O, large:O, target:O, audiences:O, ,:O, which:O, maximized:O, the:O, likelihood:O, of:O, success:O, with:O, very:O, little:O, risk:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Unit 42 tracks roughly 300 SilverTerrier actors who have registered a combined 11,600 domains over the past five years .' [/INST] [Unit:B-SecTeam, 42:I-SecTeam, tracks:O, roughly:O, 300:O, SilverTerrier:B-HackOrg, actors:I-HackOrg, who:O, have:O, registered:O, a:O, combined:O, 11,600:O, domains:O, over:O, the:O, past:O, five:O, years:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'To support the rapid growth and pace of malware distribution efforts , SilverTerrier actors are in constant need of domains to serve as C2 nodes .' [/INST] [To:O, support:O, the:O, rapid:O, growth:O, and:O, pace:O, of:O, malware:O, distribution:O, efforts:O, ,:O, SilverTerrier:B-HackOrg, actors:I-HackOrg, are:O, in:O, constant:O, need:O, of:O, domains:O, to:O, serve:O, as:O, C2:O, nodes:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'To that end , it is very unlikely that the United States government or Shell , a global energy company , would commission SilverTerrier actors to develop domains that impersonate their own legitimate websites and services .' [/INST] [To:O, that:O, end:O, ,:O, it:O, is:O, very:O, unlikely:O, that:O, the:O, United:B-Area, States:I-Area, government:B-Idus, or:O, Shell:O, ,:O, a:O, global:B-Org, energy:I-Org, company:I-Org, ,:O, would:O, commission:O, SilverTerrier:B-HackOrg, actors:I-HackOrg, to:O, develop:O, domains:O, that:O, impersonate:O, their:O, own:O, legitimate:O, websites:O, and:O, services:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The credentials they use to register their malware infrastructure are easily associated with their public social media accounts on Google® , Facebook® , MySpace® , Instagram® , and various dating and blogging sites .' [/INST] [The:O, credentials:O, they:O, use:O, to:O, register:O, their:O, malware:O, infrastructure:O, are:O, easily:O, associated:O, with:O, their:O, public:O, social:B-Idus, media:I-Idus, accounts:O, on:O, Google®:B-Org, ,:O, Facebook®:B-Org, ,:O, MySpace®:B-Org, ,:O, Instagram®:B-Org, ,:O, and:O, various:O, dating:B-Org, and:I-Org, blogging:I-Org, sites:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Earlier this year , Cybereason identified an advanced , persistent attack targeting telecommunications providers that has been underway for years , soon after deploying into the environment .' [/INST] [Earlier:O, this:O, year:O, ,:O, Cybereason:B-SecTeam, identified:O, an:O, advanced:O, ,:O, persistent:O, attack:B-OffAct, targeting:O, telecommunications:B-Org, providers:I-Org, that:O, has:O, been:O, underway:O, for:O, years:O, ,:O, soon:O, after:O, deploying:O, into:O, the:O, environment:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Based on the data available to us , Operation Soft Cell has been active since at least 2012 , though some evidence suggests even earlier activity by the threat actor against telecommunications providers .' [/INST] [Based:O, on:O, the:O, data:O, available:O, to:O, us:O, ,:O, Operation:B-OffAct, Soft:I-OffAct, Cell:I-OffAct, has:O, been:O, active:O, since:O, at:O, least:O, 2012:B-Time, ,:O, though:O, some:O, evidence:O, suggests:O, even:O, earlier:O, activity:B-OffAct, by:O, the:O, threat:B-HackOrg, actor:I-HackOrg, against:O, telecommunications:B-Org, providers:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Threat actors , especially those at the level of nation state , are seeking opportunities to attack these organizations , conducting elaborate , advanced operations to gain leverage , seize strategic assets , and collect information .' [/INST] [Threat:B-HackOrg, actors:I-HackOrg, ,:O, especially:O, those:O, at:O, the:O, level:O, of:O, nation:O, state:O, ,:O, are:O, seeking:O, opportunities:O, to:O, attack:O, these:O, organizations:O, ,:O, conducting:O, elaborate:O, ,:O, advanced:O, operations:O, to:O, gain:O, leverage:O, ,:O, seize:O, strategic:O, assets:O, ,:O, and:O, collect:O, information:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The tools and techniques used throughout these attacks are consistent with several Chinese threat actors , such as APT10 , a threat actor believed to operate on behalf of the Chinese Ministry of State Security .' [/INST] [The:O, tools:O, and:O, techniques:O, used:O, throughout:O, these:O, attacks:B-OffAct, are:O, consistent:O, with:O, several:O, Chinese:B-Area, threat:B-HackOrg, actors:I-HackOrg, ,:O, such:O, as:O, APT10:B-HackOrg, ,:O, a:O, threat:B-HackOrg, actor:I-HackOrg, believed:O, to:O, operate:O, on:O, behalf:O, of:O, the:O, Chinese:B-Area, Ministry:O, of:O, State:O, Security:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The threat actor attempted to compromise critical assets , such as database servers , billing servers , and the active directory .' [/INST] [The:O, threat:B-HackOrg, actor:I-HackOrg, attempted:O, to:O, compromise:B-Purp, critical:I-Purp, assets:I-Purp, ,:O, such:O, as:O, database:B-Purp, servers:I-Purp, ,:O, billing:B-Purp, servers:I-Purp, ,:O, and:O, the:O, active:B-Purp, directory:I-Purp, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The attack began with a web shell running on a vulnerable , publicly-facing server , from which the attackers gathered information about the network and propagated across the network .' [/INST] [The:O, attack:B-OffAct, began:O, with:O, a:O, web:B-Tool, shell:I-Tool, running:O, on:O, a:O, vulnerable:O, ,:O, publicly-facing:O, server:O, ,:O, from:O, which:O, the:O, attackers:B-HackOrg, gathered:O, information:O, about:O, the:O, network:O, and:O, propagated:O, across:O, the:O, network:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The initial indicator of the attack was a malicious web shell that was detected on an IIS server , coming out of the w3wp.exe process .' [/INST] [The:O, initial:O, indicator:O, of:O, the:O, attack:B-OffAct, was:O, a:O, malicious:O, web:O, shell:O, that:O, was:O, detected:O, on:O, an:O, IIS:O, server:O, ,:O, coming:O, out:O, of:O, the:O, w3wp.exe:B-SamFile, process:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'An investigation of the web shell , later classified as a modified version of the China Chopper web shell , uncovered several attack phases and TTPs .' [/INST] [An:O, investigation:O, of:O, the:O, web:O, shell:O, ,:O, later:O, classified:O, as:O, a:O, modified:O, version:O, of:O, the:O, China:B-Tool, Chopper:I-Tool, web:I-Tool, shell:I-Tool, ,:O, uncovered:O, several:O, attack:B-OffAct, phases:O, and:O, TTPs:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The threat actor was able to leverage the web shell to run reconnaissance commands , steal credentials , and deploy other tools .' [/INST] [The:O, threat:B-HackOrg, actor:I-HackOrg, was:O, able:O, to:O, leverage:O, the:O, web:B-Tool, shell:I-Tool, to:O, run:O, reconnaissance:O, commands:O, ,:O, steal:B-Purp, credentials:I-Purp, ,:O, and:O, deploy:O, other:O, tools:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The web shell parameters in this attack match to the China Chopper parameters , as described in FireEye 's analysis of China Chopper .' [/INST] [The:O, web:O, shell:O, parameters:O, in:O, this:O, attack:B-OffAct, match:O, to:O, the:O, China:B-HackOrg, Chopper:I-HackOrg, parameters:O, ,:O, as:O, described:O, in:O, FireEye:B-SecTeam, 's:O, analysis:O, of:O, China:B-HackOrg, Chopper:I-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It is used to remotely control web servers , and has been used in many attacks against Australian web hosting providers .' [/INST] [It:O, is:O, used:O, to:O, remotely:O, control:O, web:O, servers:O, ,:O, and:O, has:O, been:O, used:O, in:O, many:O, attacks:B-OffAct, against:O, Australian:B-Area, web:O, hosting:B-Org, providers:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This tool has been used by several Chinese-affiliated threat actors , such as APT 27 and APT 40 .' [/INST] [This:O, tool:O, has:O, been:O, used:O, by:O, several:O, Chinese-affiliated:B-Area, threat:B-HackOrg, actors:I-HackOrg, ,:O, such:O, as:O, APT:B-HackOrg, 27:I-HackOrg, and:O, APT:B-HackOrg, 40:I-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The most common credential stealing tool used by the threat actor was a modified mimikatz that dumps NTLM hashes .' [/INST] [The:O, most:O, common:O, credential:O, stealing:O, tool:O, used:O, by:O, the:O, threat:B-HackOrg, actor:I-HackOrg, was:O, a:O, modified:O, mimikatz:B-Tool, that:O, dumps:B-Features, NTLM:I-Features, hashes:I-Features, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The threat actor relied on WMI and PsExec to move laterally and install their tools across multiple assets .' [/INST] [The:O, threat:B-HackOrg, actor:I-HackOrg, relied:O, on:O, WMI:B-Tool, and:O, PsExec:B-Tool, to:O, move:O, laterally:O, and:O, install:O, their:O, tools:O, across:O, multiple:O, assets:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Nbtscan has been used by APT10 in Operation Cloud Hopper to search for services of interest across the IT estate and footprint endpoints of interest .' [/INST] [Nbtscan:B-Tool, has:O, been:O, used:O, by:O, APT10:B-HackOrg, in:O, Operation:B-OffAct, Cloud:I-OffAct, Hopper:I-OffAct, to:O, search:O, for:O, services:O, of:O, interest:O, across:O, the:O, IT:B-Idus, estate:O, and:O, footprint:O, endpoints:O, of:O, interest:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'A second method the threat actor used to maintain access across the compromised assets was through the deployment of the PoisonIvy RAT ( PIVY ) .' [/INST] [A:O, second:O, method:O, the:O, threat:B-HackOrg, actor:I-HackOrg, used:O, to:O, maintain:O, access:O, across:O, the:O, compromised:O, assets:O, was:O, through:O, the:O, deployment:O, of:O, the:O, PoisonIvy:B-Tool, RAT:I-Tool, (:O, PIVY:B-Tool, ):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This infamous RAT has been associated with many different Chinese threat actors , including APT10 , APT1 , and DragonOK .' [/INST] [This:O, infamous:O, RAT:B-Tool, has:O, been:O, associated:O, with:O, many:O, different:O, Chinese:B-Area, threat:B-HackOrg, actors:I-HackOrg, ,:O, including:O, APT10:B-HackOrg, ,:O, APT1:B-HackOrg, ,:O, and:O, DragonOK:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It is a powerful , multi-featured RAT that lets a threat actor take total control over a machine .' [/INST] [It:O, is:O, a:O, powerful:O, ,:O, multi-featured:B-Tool, RAT:I-Tool, that:O, lets:O, a:O, threat:B-HackOrg, actor:I-HackOrg, take:O, total:O, control:O, over:O, a:O, machine:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In an attempt to hide the contents of the stolen data , the threat actor used winrar to compress and password-protect it .' [/INST] [In:O, an:O, attempt:O, to:O, hide:O, the:O, contents:O, of:O, the:O, stolen:O, data:O, ,:O, the:O, threat:B-HackOrg, actor:I-HackOrg, used:O, winrar:B-Tool, to:O, compress:O, and:O, password-protect:O, it:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The winrar binaries and compressed data were found mostly in the Recycle Bin folder , a TTP that was previously observed in APT10-related attacks , as well as others .' [/INST] [The:O, winrar:B-Tool, binaries:O, and:O, compressed:O, data:O, were:O, found:O, mostly:O, in:O, the:O, Recycle:B-Tool, Bin:I-Tool, folder:I-Tool, ,:O, a:O, TTP:B-Tool, that:O, was:O, previously:O, observed:O, in:O, APT10-related:B-OffAct, attacks:I-OffAct, ,:O, as:O, well:O, as:O, others:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This ' connection bouncer ' tool lets the threat actor redirect ports and connections between different networks and obfuscate C2 server traffic .' [/INST] [This:O, ':O, connection:B-Tool, bouncer:I-Tool, ':O, tool:O, lets:O, the:O, threat:B-HackOrg, actor:I-HackOrg, redirect:O, ports:B-Features, and:I-Features, connections:I-Features, between:O, different:O, networks:O, and:O, obfuscate:O, C2:O, server:O, traffic:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In order to exfiltrate data from a network segment not connected to the Internet , the threat actor deployed a modified version of hTran .' [/INST] [In:O, order:O, to:O, exfiltrate:B-Purp, data:I-Purp, from:I-Purp, a:O, network:O, segment:O, not:O, connected:O, to:O, the:O, Internet:O, ,:O, the:O, threat:B-HackOrg, actor:I-HackOrg, deployed:O, a:O, modified:O, version:O, of:O, hTran:B-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'There have been numerous reports of hTran being used by different Chinese threat actors , including : APT3 , APT27 and DragonOK .' [/INST] [There:O, have:O, been:O, numerous:O, reports:O, of:O, hTran:B-Tool, being:O, used:O, by:O, different:O, Chinese:B-Area, threat:B-HackOrg, actors:I-HackOrg, ,:O, including:O, ::O, APT3:B-HackOrg, ,:O, APT27:B-HackOrg, and:O, DragonOK:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The threat actor made some modifications to the original source code of hTran .' [/INST] [The:O, threat:B-HackOrg, actor:I-HackOrg, made:O, some:O, modifications:O, to:O, the:O, original:O, source:O, code:O, of:O, hTran:B-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The threat actor had a specific pattern of behavior that allowed us to understand their modus operandi : they used one server with the same IP address for multiple operations .' [/INST] [The:O, threat:B-HackOrg, actor:I-HackOrg, had:O, a:O, specific:O, pattern:O, of:O, behavior:O, that:O, allowed:O, us:O, to:O, understand:O, their:O, modus:O, operandi:O, ::O, they:O, used:O, one:O, server:O, with:O, the:O, same:O, IP:O, address:O, for:O, multiple:O, operations:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'There are previous reports of threat actors including APT10 and APT1 using dynamic DNS .' [/INST] [There:O, are:O, previous:O, reports:O, of:O, threat:B-HackOrg, actors:I-HackOrg, including:O, APT10:B-HackOrg, and:O, APT1:B-HackOrg, using:O, dynamic:B-Tool, DNS:I-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Our investigation showed that these attacks were targeted , and that the threat actor sought to steal communications data of specific individuals in various countries .' [/INST] [Our:O, investigation:O, showed:O, that:O, these:O, attacks:B-OffAct, were:O, targeted:O, ,:O, and:O, that:O, the:O, threat:B-HackOrg, actor:I-HackOrg, sought:O, to:O, steal:B-Purp, communications:I-Purp, data:I-Purp, of:O, specific:B-Org, individuals:I-Org, in:O, various:O, countries:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The data exfiltrated by this threat actor , in conjunction with the TTPs and tools used , allowed us to determine with a very high probability that the threat actor behind these malicious operations is backed by a nation state , and is affiliated with China .' [/INST] [The:O, data:O, exfiltrated:O, by:O, this:O, threat:B-HackOrg, actor:I-HackOrg, ,:O, in:O, conjunction:O, with:O, the:O, TTPs:O, and:O, tools:O, used:O, ,:O, allowed:O, us:O, to:O, determine:O, with:O, a:O, very:O, high:O, probability:O, that:O, the:O, threat:B-HackOrg, actor:I-HackOrg, behind:O, these:O, malicious:O, operations:O, is:O, backed:O, by:O, a:O, nation:O, state:O, ,:O, and:O, is:O, affiliated:O, with:O, China:B-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Symantec saw the first evidence of Sowbug-related activity with the discovery in March 2017 of an entirely new piece of malware called Felismus used against a target in Southeast Asia .' [/INST] [Symantec:B-SecTeam, saw:O, the:O, first:O, evidence:O, of:O, Sowbug-related:B-OffAct, activity:I-OffAct, with:O, the:O, discovery:O, in:O, March:B-Time, 2017:I-Time, of:O, an:O, entirely:O, new:O, piece:O, of:O, malware:O, called:O, Felismus:B-Tool, used:O, against:O, a:O, target:O, in:O, Southeast:B-Area, Asia:I-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Symantec saw the first evidence of Sowbug group with the discovery in March 2017 of an entirely new piece of malware called Felismus used against a target in Southeast Asia .' [/INST] [Symantec:B-SecTeam, saw:O, the:O, first:O, evidence:O, of:O, Sowbug:B-HackOrg, group:I-HackOrg, with:O, the:O, discovery:O, in:O, March:B-Time, 2017:I-Time, of:O, an:O, entirely:O, new:O, piece:O, of:O, malware:O, called:O, Felismus:B-Tool, used:O, against:O, a:O, target:O, in:O, Southeast:B-Area, Asia:I-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Symantec has also been able to connect earlier attack campaigns with Sowbug , demonstrating that it has been active since at least early-2015 and may have been operating even earlier .' [/INST] [Symantec:B-SecTeam, has:O, also:O, been:O, able:O, to:O, connect:O, earlier:O, attack:B-OffAct, campaigns:I-OffAct, with:O, Sowbug:B-HackOrg, ,:O, demonstrating:O, that:O, it:O, has:O, been:O, active:O, since:O, at:O, least:O, early-2015:B-Time, and:O, may:O, have:O, been:O, operating:O, even:O, earlier:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'To date , Sowbug appears to be focused mainly on government entities in South America and Southeast Asia and has infiltrated organizations in Argentina , Brazil , Ecuador , Peru , Brunei and Malaysia .' [/INST] [To:O, date:O, ,:O, Sowbug:B-HackOrg, appears:O, to:O, be:O, focused:O, mainly:O, on:O, government:B-Org, entities:I-Org, in:O, South:B-Area, America:I-Area, and:O, Southeast:B-Area, Asia:I-Area, and:O, has:O, infiltrated:B-Org, organizations:I-Org, in:O, Argentina:B-Area, ,:O, Brazil:B-Area, ,:O, Ecuador:B-Area, ,:O, Peru:B-Area, ,:O, Brunei:B-Area, and:O, Malaysia:B-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'For example , in a 2015 attack on one South American foreign ministry , the group appeared to be searching for very specific information .' [/INST] [For:O, example:O, ,:O, in:O, a:O, 2015:B-OffAct, attack:I-OffAct, on:O, one:O, South:B-Area, American:I-Area, foreign:O, ministry:O, ,:O, the:O, group:B-HackOrg, appeared:O, to:O, be:O, searching:O, for:O, very:O, specific:O, information:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The first evidence of its intrusion dated from May 6 , 2015 but activity appeared to have begun in earnest on May 12 .' [/INST] [The:O, first:O, evidence:O, of:O, its:O, intrusion:O, dated:O, from:O, May:B-Time, 6:I-Time, ,:I-Time, 2015:I-Time, but:O, activity:B-OffAct, appeared:O, to:O, have:O, begun:O, in:O, earnest:O, on:O, May:B-Time, 12:I-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In total , the attackers maintained a presence on the target 's network for four months between May and September 2015 .' [/INST] [In:O, total:O, ,:O, the:O, attackers:B-HackOrg, maintained:O, a:O, presence:O, on:O, the:O, target:O, 's:O, network:O, for:O, four:O, months:O, between:O, May:B-Time, and:O, September:B-Time, 2015:I-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We have previously detected groups we suspect are affiliated with the North Korean government compromising electric utilities in South Korea , but these compromises did not lead to a disruption of the power supply .' [/INST] [We:O, have:O, previously:O, detected:O, groups:B-HackOrg, we:O, suspect:O, are:O, affiliated:O, with:O, the:O, North:B-Area, Korean:I-Area, government:B-Org, compromising:O, electric:B-Idus, utilities:O, in:O, South:B-Area, Korea:I-Area, ,:O, but:O, these:O, compromises:O, did:O, not:O, lead:O, to:O, a:O, disruption:O, of:O, the:O, power:O, supply:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Instead , sensitive KHNP documents were leaked by the actors as part of an effort to exaggerate the access they had and embarrass the South Korean Government , a technique we assess North Korea would turn to again in order to instill fear and/or meet domestic propaganda aims .' [/INST] [Instead:O, ,:O, sensitive:O, KHNP:B-SamFile, documents:I-SamFile, were:O, leaked:O, by:O, the:O, actors:B-HackOrg, as:O, part:O, of:O, an:O, effort:O, to:O, exaggerate:O, the:O, access:O, they:O, had:O, and:O, embarrass:O, the:O, South:B-Org, Korean:I-Org, Government:I-Org, ,:O, a:O, technique:O, we:O, assess:O, North:B-Area, Korea:I-Area, would:O, turn:O, to:O, again:O, in:O, order:O, to:O, instill:O, fear:O, and/or:O, meet:O, domestic:O, propaganda:O, aims:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'North Korea linked hackers are among the most prolific nation-state threats , targeting not only the U.S. and South Korea but the global financial system and nations worldwide .' [/INST] [North:B-Area, Korea:I-Area, linked:O, hackers:O, are:O, among:O, the:O, most:O, prolific:O, nation-state:O, threats:O, ,:O, targeting:O, not:O, only:O, the:B-Area, U.S.:I-Area, and:O, South:B-Area, Korea:I-Area, but:O, the:O, global:O, financial:B-Idus, system:O, and:O, nations:B-Idus, worldwide:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FireEye has detected more than 20 cyber threat groups suspected to be sponsored by at least four other nation-states attempting to gain access to targets in the energy sector that could have been used to cause disruptions .' [/INST] [FireEye:B-SecTeam, has:O, detected:O, more:O, than:O, 20:O, cyber:O, threat:O, groups:B-HackOrg, suspected:O, to:O, be:O, sponsored:O, by:O, at:O, least:O, four:O, other:O, nation-states:O, attempting:O, to:O, gain:O, access:O, to:O, targets:O, in:O, the:O, energy:O, sector:O, that:O, could:O, have:O, been:O, used:O, to:O, cause:O, disruptions:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'CapabilitiesFormBook is a data stealer , but not a full-fledged banker .' [/INST] [CapabilitiesFormBook:B-Idus, is:O, a:O, data:O, stealer:O, ,:O, but:O, not:O, a:O, full-fledged:O, banker:B-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'FormBook OverviewFormBook is a data stealer and form grabber that has been advertised in various hacking forums since early 2016 .' [/INST] [FormBook:B-Tool, OverviewFormBook:I-Tool, is:O, a:O, data:O, stealer:O, and:O, form:O, grabber:O, that:O, has:O, been:O, advertised:O, in:O, various:O, hacking:O, forums:O, since:O, early:O, 2016:B-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The malware may inject itself into browser processes and explorer.exe .' [/INST] [The:O, malware:B-Tool, may:O, inject:B-Way, itself:I-Way, into:O, browser:O, processes:O, and:O, explorer.exe:B-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The attackers involved in these email campaigns leveraged a variety of distribution mechanisms to deliver the information stealing FormBook malware .' [/INST] [The:O, attackers:B-HackOrg, involved:O, in:O, these:O, email:O, campaigns:O, leveraged:O, a:O, variety:O, of:O, distribution:O, mechanisms:O, to:O, deliver:B-Purp, the:I-Purp, information:I-Purp, stealing:O, FormBook:O, malware:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Much of the activity was observed in the United States (Figure 11) , and the most targeted industry vertical was Aerospace/Defense Contractors (Figure 12) .' [/INST] [Much:O, of:O, the:O, activity:O, was:O, observed:O, in:O, the:B-Area, United:I-Area, States:I-Area, (Figure:O, 11):O, ,:O, and:O, the:O, most:O, targeted:O, industry:O, vertical:O, was:O, Aerospace/Defense:B-Idus, Contractors:O, (Figure:O, 12):O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the last few weeks , FormBook was seen downloading other malware families such as NanoCore .' [/INST] [In:O, the:O, last:B-Time, few:I-Time, weeks:I-Time, ,:O, FormBook:B-SamFile, was:O, seen:O, downloading:O, other:O, malware:O, families:O, such:O, as:O, NanoCore:B-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We have associated this campaign with APT19 , a group that we assess is composed of freelancers , with some degree of sponsorship by the Chinese government .' [/INST] [We:O, have:O, associated:O, this:O, campaign:B-OffAct, with:O, APT19:B-HackOrg, ,:O, a:O, group:O, that:O, we:O, assess:O, is:O, composed:O, of:O, freelancers:O, ,:O, with:O, some:O, degree:O, of:O, sponsorship:O, by:O, the:O, Chinese:B-Org, government:B-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The vulnerability is bypassing most mitigations; however , as noted above , FireEye email and network products detect the malicious documents .' [/INST] [The:O, vulnerability:O, is:O, bypassing:O, most:O, mitigations;:O, however:O, ,:O, as:O, noted:O, above:O, ,:O, FireEye:B-SecTeam, email:O, and:O, network:O, products:O, detect:O, the:O, malicious:B-SamFile, documents:I-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'We have previously observed APT19 steal data from law and investment firms for competitive economic purposes .' [/INST] [We:O, have:O, previously:O, observed:O, APT19:B-HackOrg, steal:B-OffAct, data:I-OffAct, from:O, law:O, and:O, investment:O, firms:O, for:O, competitive:B-Purp, economic:I-Purp, purposes:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Through the exploitation of the HTA handler vulnerability described in CVE-2017-1099 , the observed RTF attachments download .' [/INST] [Through:O, the:O, exploitation:O, of:O, the:O, HTA:O, handler:O, vulnerability:O, described:O, in:O, CVE-2017-1099:B-Exp, ,:O, the:O, observed:O, RTF:B-SamFile, attachments:I-SamFile, download:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In early May , the phishing lures leveraged RTF attachments that exploited the Microsoft Windows vulnerability described in CVE-2017-0199 .' [/INST] [In:O, early:O, May:B-Time, ,:O, the:O, phishing:B-Way, lures:I-Way, leveraged:O, RTF:B-SamFile, attachments:I-SamFile, that:O, exploited:O, the:O, Microsoft:O, Windows:O, vulnerability:O, described:O, in:O, CVE-2017-0199:B-Exp, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Furthermore , there are indications that APT32 actors are targeting peripheral network security and technology infrastructure corporations .' [/INST] [Furthermore:O, ,:O, there:O, are:O, indications:O, that:O, APT32:B-HackOrg, actors:O, are:O, targeting:O, peripheral:O, network:O, security:B-Idus, and:O, technology:B-Idus, infrastructure:O, corporations:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'This focused intelligence and detection effort led to new external victim identifications as well as providing sufficient technical evidence to link twelve prior intrusions , consolidating four previously unrelated clusters of threat actor activity into FireEye’s newest named advanced persistent threat group: APT32 .' [/INST] [This:O, focused:O, intelligence:O, and:O, detection:O, effort:O, led:O, to:O, new:O, external:O, victim:O, identifications:O, as:O, well:O, as:O, providing:O, sufficient:O, technical:O, evidence:O, to:O, link:O, twelve:O, prior:O, intrusions:O, ,:O, consolidating:O, four:O, previously:O, unrelated:O, clusters:O, of:O, threat:O, actor:O, activity:O, into:O, FireEye’s:B-SecTeam, newest:O, named:O, advanced:O, persistent:O, threat:O, group::O, APT32:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In mid-2016 , malware that FireEye believes to be unique to APT32 was detected on the networks of a global hospitality industry developer with plans to expand operations into Vietnam .' [/INST] [In:O, mid-2016:B-SecTeam, ,:O, malware:O, that:O, FireEye:B-SecTeam, believes:O, to:O, be:O, unique:O, to:O, APT32:B-HackOrg, was:O, detected:O, on:O, the:O, networks:O, of:O, a:O, global:O, hospitality:B-Idus, industry:O, developer:O, with:O, plans:O, to:O, expand:O, operations:O, into:O, Vietnam:B-Area, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In March 2017 , in response to active targeting of FireEye clients , the team launched a Community Protection Event (CPE) – a coordinated effort between Mandiant incident responders , FireEye as a Service (FaaS) , FireEye iSight Intelligence , and FireEye product engineering – to protect all clients from APT32 activity .' [/INST] [In:O, March:B-Time, 2017:I-Time, ,:O, in:O, response:O, to:O, active:O, targeting:O, of:O, FireEye:B-SecTeam, clients:O, ,:O, the:O, team:O, launched:O, a:O, Community:B-OffAct, Protection:I-OffAct, Event:I-OffAct, (CPE):O, –:O, a:O, coordinated:O, effort:O, between:O, Mandiant:B-SecTeam, incident:O, responders:O, ,:O, FireEye:B-SecTeam, as:O, a:O, Service:O, (FaaS):O, ,:O, FireEye:B-SecTeam, iSight:I-SecTeam, Intelligence:I-SecTeam, ,:O, and:O, FireEye:B-SecTeam, product:O, engineering:O, –:O, to:O, protect:O, all:O, clients:O, from:O, APT32:B-HackOrg, activity:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In their current campaign , APT32 has leveraged ActiveMime files that employ social engineering methods to entice the victim into enabling macros .' [/INST] [In:O, their:O, current:O, campaign:B-OffAct, ,:O, APT32:B-HackOrg, has:O, leveraged:O, ActiveMime:B-SamFile, files:I-SamFile, that:O, employ:O, social:B-Way, engineering:I-Way, methods:O, to:O, entice:O, the:O, victim:O, into:O, enabling:O, macros:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'APT32 actors continue to deliver the malicious attachments via spear-phishing emails .' [/INST] [APT32:B-HackOrg, actors:O, continue:O, to:O, deliver:O, the:O, malicious:B-SamFile, attachments:I-SamFile, via:O, spear-phishing:B-Way, emails:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'APT19 leveraged Rich Text Format (RTF) and macro-enabled Microsoft Excel files to deliver their initial exploits .' [/INST] [APT19:B-HackOrg, leveraged:O, Rich:O, Text:O, Format:O, (RTF):O, and:O, macro-enabled:O, Microsoft:B-SamFile, Excel:I-SamFile, files:I-SamFile, to:O, deliver:B-Purp, their:O, initial:O, exploits:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In the following weeks , FireEye released threat intelligence products and updated malware profiles to customers while developing new detection techniques for APT32’s tools and phishing lures .' [/INST] [In:O, the:O, following:O, weeks:O, ,:O, FireEye:B-SecTeam, released:O, threat:O, intelligence:O, products:O, and:O, updated:O, malware:O, profiles:O, to:O, customers:O, while:O, developing:O, new:O, detection:O, techniques:O, for:O, APT32’s:B-HackOrg, tools:O, and:O, phishing:B-Way, lures:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Also in 2014 , APT32 carried out an intrusion against a Western country’s national legislature .' [/INST] [Also:O, in:O, 2014:B-Time, ,:O, APT32:B-HackOrg, carried:O, out:O, an:O, intrusion:O, against:O, a:O, Western:B-Area, country’s:O, national:O, legislature:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In 2015 , SkyEye Labs , the security research division of the Chinese firm Qihoo 360 , released a report detailing threat actors that were targeting Chinese public and private entities including government agencies , research institutes , maritime agencies , sea construction , and shipping enterprises .' [/INST] [In:O, 2015:B-Time, ,:O, SkyEye:B-SecTeam, Labs:I-SecTeam, ,:O, the:O, security:O, research:O, division:O, of:O, the:O, Chinese:B-Area, firm:O, Qihoo:B-SecTeam, 360:I-SecTeam, ,:O, released:O, a:O, report:O, detailing:O, threat:O, actors:O, that:O, were:O, targeting:O, Chinese:B-Area, public:O, and:O, private:O, entities:O, including:O, government:B-Org, agencies:I-Org, ,:O, research:B-Org, institutes:I-Org, ,:O, maritime:B-Org, agencies:I-Org, ,:O, sea:B-Org, construction:I-Org, ,:O, and:O, shipping:B-Org, enterprises:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'In order to track who opened the phishing emails , viewed the links , and downloaded the attachments in real-time , APT32 used cloud-based email analytics software designed for sales organizations .' [/INST] [In:O, order:O, to:O, track:O, who:O, opened:O, the:O, phishing:B-Way, emails:O, ,:O, viewed:O, the:O, links:O, ,:O, and:O, downloaded:O, the:O, attachments:O, in:O, real-time:O, ,:O, APT32:B-HackOrg, used:O, cloud-based:O, email:O, analytics:O, software:O, designed:O, for:O, sales:B-Org, organizations:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Since at least 2014 , FireEye has observed APT32 targeting foreign corporations with a vested interest in Vietnama's manufacturing , consumer products , and hospitality sectors .' [/INST] [Since:O, at:O, least:O, 2014:B-Time, ,:O, FireEye:B-SecTeam, has:O, observed:O, APT32:B-HackOrg, targeting:O, foreign:O, corporations:O, with:O, a:O, vested:O, interest:O, in:O, Vietnama's:B-Area, manufacturing:B-Org, ,:O, consumer:B-Org, products:I-Org, ,:O, and:O, hospitality:B-Org, sectors:I-Org, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Mandiant consultants suspect that APT32 was monitoring web logs to track the public IP address used to request remote images .' [/INST] [Mandiant:B-SecTeam, consultants:O, suspect:O, that:O, APT32:B-HackOrg, was:O, monitoring:O, web:O, logs:O, to:O, track:B-Purp, the:I-Purp, public:I-Purp, IP:I-Purp, address:I-Purp, used:O, to:O, request:O, remote:O, images:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'APT32 often deploys these backdoors along with the commercially-available Cobalt Strike BEACON backdoor .' [/INST] [APT32:B-HackOrg, often:O, deploys:O, these:O, backdoors:B-Tool, along:O, with:O, the:O, commercially-available:O, Cobalt:B-HackOrg, Strike:I-HackOrg, BEACON:B-Tool, backdoor:B-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'The targeting of private sector interests by APT32 is notable and FireEye believes the actor poses significant risk to companies doing business in , or preparing to invest in , the country .' [/INST] [The:O, targeting:O, of:O, private:O, sector:O, interests:O, by:O, APT32:B-HackOrg, is:O, notable:O, and:O, FireEye:B-SecTeam, believes:O, the:O, actor:O, poses:O, significant:O, risk:O, to:O, companies:O, doing:O, business:B-Idus, in:O, ,:O, or:O, preparing:O, to:O, invest:O, in:O, ,:O, the:O, country:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While the motivation for each APT32 private sector compromise varied – and in some cases was unknown – the unauthorized access could serve as a platform for law enforcement , intellectual property theft , or anticorruption measures that could ultimately erode the competitive advantage of targeted organizations .' [/INST] [While:O, the:O, motivation:O, for:O, each:O, APT32:B-HackOrg, private:O, sector:O, compromise:O, varied:O, –:O, and:O, in:O, some:O, cases:O, was:O, unknown:O, –:O, the:O, unauthorized:O, access:O, could:O, serve:O, as:O, a:O, platform:O, for:O, law:B-Idus, enforcement:I-Idus, ,:O, intellectual:O, property:O, theft:O, ,:O, or:O, anticorruption:O, measures:O, that:O, could:O, ultimately:O, erode:O, the:O, competitive:O, advantage:O, of:O, targeted:O, organizations:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'While actors from China , Iran , Russia , and North Korea remain the most active cyber espionage threats tracked and responded to by FireEye , APT32 reflects a growing host of new countries that have adopted this dynamic capability .' [/INST] [While:O, actors:O, from:O, China:B-Area, ,:O, Iran:B-Area, ,:O, Russia:B-Area, ,:O, and:O, North:B-Area, Korea:I-Area, remain:O, the:O, most:O, active:O, cyber:O, espionage:O, threats:O, tracked:O, and:O, responded:O, to:O, by:O, FireEye:B-SecTeam, ,:O, APT32:B-HackOrg, reflects:O, a:O, growing:O, host:O, of:O, new:O, countries:O, that:O, have:O, adopted:O, this:O, dynamic:O, capability:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Several Mandiant investigations revealed that , after gaining access , APT32 regularly cleared select event log entries and heavily obfuscated their PowerShell-based tools and shellcode loaders with Daniel Bohannon’s Invoke-Obfuscation framework .' [/INST] [Several:O, Mandiant:B-SecTeam, investigations:O, revealed:O, that:O, ,:O, after:O, gaining:O, access:O, ,:O, APT32:B-HackOrg, regularly:O, cleared:O, select:O, event:O, log:O, entries:O, and:O, heavily:O, obfuscated:O, their:O, PowerShell-based:B-Tool, tools:I-Tool, and:O, shellcode:O, loaders:O, with:O, Daniel:O, Bohannon’s:O, Invoke-Obfuscation:O, framework:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Furthermore , APT32 continues to threaten political activism and free speech in Southeast Asia and the public sector worldwide .' [/INST] [Furthermore:O, ,:O, APT32:B-HackOrg, continues:O, to:O, threaten:O, political:O, activism:O, and:O, free:O, speech:O, in:O, Southeast:B-Area, Asia:I-Area, and:O, the:O, public:B-Org, sector:I-Org, worldwide:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'North Korea's Office 39 is involved in activities such as gold smuggling , counterfeiting foreign currency , and even operating restaurants .' [/INST] [North:B-Area, Korea's:I-Area, Office:O, 39:O, is:O, involved:O, in:O, activities:B-OffAct, such:O, as:O, gold:O, smuggling:O, ,:O, counterfeiting:O, foreign:O, currency:O, ,:O, and:O, even:O, operating:O, restaurants:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'With these details , we will then draw some conclusions about the operators of CARBANAK .' [/INST] [With:O, these:O, details:O, ,:O, we:O, will:O, then:O, draw:O, some:O, conclusions:O, about:O, the:O, operators:O, of:O, CARBANAK:B-HackOrg, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Most of these data-stealing capabilities were present in the oldest variants of CARBANAK that we have seen and some were added over time .' [/INST] [Most:O, of:O, these:O, data-stealing:B-Purp, capabilities:O, were:O, present:O, in:O, the:O, oldest:O, variants:O, of:O, CARBANAK:B-SamFile, that:O, we:O, have:O, seen:O, and:O, some:O, were:O, added:O, over:O, time:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Since May 2017 , Mandiant experts observed North Korean actors target at least three South Korean cryptocurrency exchanges with the suspected intent of stealing funds .' [/INST] [Since:O, May:B-Time, 2017:I-Time, ,:O, Mandiant:B-SecTeam, experts:O, observed:O, North:B-Area, Korean:I-Area, actors:O, target:O, at:O, least:O, three:O, South:B-Area, Korean:I-Area, cryptocurrency:O, exchanges:O, with:O, the:O, suspected:O, intent:O, of:O, stealing:B-Purp, funds:I-Purp, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'February saw three particularly interesting publications on the topic of macOS malware: a Trojan Cocoa application that sends system information including keychain data back to the attacker , a macOS version of APT28’s Xagent malware , and a new Trojan ransomware .' [/INST] [February:B-Time, saw:O, three:O, particularly:O, interesting:O, publications:O, on:O, the:O, topic:O, of:O, macOS:O, malware::O, a:O, Trojan:O, Cocoa:O, application:O, that:O, sends:O, system:O, information:O, including:O, keychain:O, data:O, back:O, to:O, the:O, attacker:B-HackOrg, ,:O, a:O, macOS:O, version:O, of:O, APT28’s:B-HackOrg, Xagent:O, malware:O, ,:O, and:O, a:O, new:O, Trojan:B-SamFile, ransomware:I-SamFile, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'Per a 2015 report from CitizenLab , Gamma Group licenses their software to clients and each client uses unique infrastructure , making it likely that the two documents are being used by a single client .' [/INST] [Per:O, a:O, 2015:B-Time, report:O, from:O, CitizenLab:B-SecTeam, ,:O, Gamma:B-HackOrg, Group:I-HackOrg, licenses:O, their:O, software:O, to:O, clients:O, and:O, each:O, client:O, uses:O, unique:O, infrastructure:B-Org, ,:O, making:O, it:O, likely:O, that:O, the:O, two:O, documents:O, are:O, being:O, used:O, by:O, a:O, single:O, client:O, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'As early as March 4 , 2017 , malicious documents exploiting CVE-2017-0199 were used to deliver the LATENTBOT malware .' [/INST] [As:O, early:O, as:O, March:B-Time, 4:I-Time, ,:O, 2017:B-Time, ,:O, malicious:B-SamFile, documents:I-SamFile, exploiting:O, CVE-2017-0199:B-Exp, were:O, used:O, to:O, deliver:O, the:O, LATENTBOT:B-Tool, malware:I-Tool, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'LATENTBOT is a modular and highly obfuscated type of malware first discovered by FireEye iSIGHT intelligence in December 2015 .' [/INST] [LATENTBOT:B-Tool, is:O, a:O, modular:O, and:O, highly:O, obfuscated:O, type:O, of:O, malware:O, first:O, discovered:O, by:O, FireEye:B-SecTeam, iSIGHT:I-SecTeam, intelligence:I-SecTeam, in:O, December:B-Time, 2015:I-Time, .:O] </s> |
<s>[INST] You are a cybersecurity and NLP expert tasked with named entity extraction. You need to provide the named entities for each token in the given sentence. You will be penalized for incorrect predictions. Identify the entities for the following sentence: 'It is capable of a variety of functions , including credential theft , hard drive and data wiping , disabling security software , and remote desktop functionality .' [/INST] [It:O, is:O, capable:O, of:O, a:O, variety:O, of:O, functions:O, ,:O, including:O, credential:B-Features, theft:I-Features, ,:O, hard:B-Features, drive:I-Features, and:O, data:B-Features, wiping:I-Features, ,:O, disabling:B-Features, security:I-Features, software:I-Features, ,:O, and:O, remote:B-Features, desktop:I-Features, functionality:I-Features, .:O] </s> |