The widespread adoption of Internet of Things has led to many security issues. Post the Mirai-based DDoS attack in 2016 which compromised IoT devices, a host of new malware using Mirai's leaked source code and targeting IoT devices have cropped up, e.g. Satori, Reaper, Amnesia, Masuta etc. These malware exploit software vulnerabilities to infect IoT devices instead of open TELNET ports (like Mirai) making them more difficult to block using existing solutions such as firewalls. In this research, we present EDIMA, a distributed modular solution which can be used towards the detection of IoT malware network activity in large-scale networks (e.g. ISP, enterprise networks) during the scanning infecting phase rather than during an attack. EDIMA employs machine learning algorithms for edge devices' traffic classification, a packet traffic feature vector database, a policy module and an optional packet sub-sampling module. We evaluate the classification performance of EDIMA through testbed experiments and present the results obtained.
Our work addresses a few important gaps in the literature when it comes to distinguishing between legitimate and botnet IoT traffic. First, the works on detecting botnets using their CnC communication features @cite_0 @cite_20 @cite_7 @cite_17 are designed for PC-based botnets rather than IoT botnets which are the focus of our work. Second, we do not aim to detect botnets (networks of bots) but instead, network activity generated by individual bots. IoT botnets tend to consist of hundreds of thousands to millions of devices spread over vast geographies, hence, it is impractical to detect a whole network of IoT bots. Therefore, we do not require computationally expensive clustering algorithms as used in @cite_7 @cite_17 .
@cite_0 @cite_7 @cite_20 @cite_17
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The proliferation of IoT systems, has seen them targeted by malicious third parties. To address this, realistic protection and investigation countermeasures need to be developed. Such countermeasures include network intrusion detection and network forensic systems. For that purpose, a well-structured and representative dataset is paramount for training and validating the credibility of the systems. Although there are several network, in most cases, not much information is given about the Botnet scenarios that were used. This paper, proposes a new dataset, Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks. We also present a realistic testbed environment for addressing the existing dataset drawbacks of capturing complete network information, accurate labeling, as well as recent and complex attack diversity. Finally, we evaluate the reliability of the BoT-IoT dataset using different statistical and machine learning methods for forensics purposes compared with the existing datasets. This work provides the baseline for allowing botnet identificaiton across IoT-specifc networks. The Bot-IoT dataset can be accessed at [1]. Botnets are now the key platform for many Internet attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of the current botnet detection approaches work only on specific botnet command and control (C&C) protocols (e.g., IRC) and structures (e.g., centralized), and can become ineffective as botnets change their C&C techniques. In this paper, we present a general detection framework that is independent of botnet C&C protocol and structure, and requires no a priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C&C server names addresses). We start from the definition and essential properties of botnets. We define a botnet as a coordinated group of malware instances that are controlled via C&C communication channels. The essential properties of a botnet are that the bots communicate with some C&C servers peers, perform malicious activities, and do so in a similar or correlated way. Accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network. We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets (IRC-based, HTTP-based, and P2P botnets including Nugache and Storm worm), and has a very low false positive rate. The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for new methods that detect attacks launched from compromised IoT devices and that differentiate between hours- and milliseconds-long IoT-based attacks. In this article, we propose a novel network-based anomaly detection method for the IoT called N-BaIoT that extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two widely known IoT-based botnets, Mirai and BASHLITE. The evaluation results demonstrated our proposed methods ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices that were part of a botnet. Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives. There is an urgent need for a pro-active approach to botnet detection and mitigation that will enable computer network defenders to characterize emerging and future botnet threats and design effective defense techniques before these threats materialize. To this end, we have developed a System for Live Investigation of Next Generation bots (SLINGbot). SLINGbot is an extensible, composable bot framework that enables researchers to construct benign bots for the purposes of generating and characterizing botnet Command and Control (C2) traffic. This enables researchers to simulate current and potential future botnet traffic, characterize it, and design effective defense techniques. In this paper, we describe the SLINGbot system and how it can be used for the pro-active development of botnet defenses. An in-depth understanding of botnet behavior is a precursor to building effective defenses against this serious and growing threat. In this paper we describe our initial steps toward building a flexible and scalable laboratory testbed for experiments with bots and botnets. Our Botnet Evaluation Environment (BEE) is designed to enable individual bots or networks of up to thousands of bots to be tested in a secure, self-contained framework. BEE is being developed as a toolkit for Emulab-enabled network testbeds; a design choice made to obviate the need for building user experiment management functions and to enable access to collections of computing hosts. The focus of our implementation efforts has been on building a library of OS Bot images that can be run on individual systems or on virtual machines. The library currently includes images generated from source code of four well known bots (Agobot, GTbot, Spybot, SDbot) and from binary code for several unknown bots, and a number of Windows OS variants. BEE also includes a set of services that are required for botnets including DHCP, DynDNS, and IRC, as well as other tools that are useful for botnet measurement and evaluation such as VM monitors and honeypots. To demonstrate the utility of BEE, we describe a simple set of tests that characterizes command and control traffic from three different botnet configurations. Automated bot botnet detection is a difficult problem given the high level of attacker power. We propose a systematic approach for evaluating the evadability of detection methods. An evasion tactic has two associated costs: implementation complexity and effect on botnet utility. An evasion tactic's implementation complexity is based on the ease with which bot writers can incrementally modify current bots to evade detection. Modifying a bot in order to evade a detection method may result in a less useful botnet; to explore this, we identify aspects of botnets that impact their revenue-generating capability. For concreteness, we survey some leading automated bot botnet detection methods, identify evasion tactics for each, and assess the costs of these tactics. We also reconsider assumptions about botnet control that underly many botnet detection methods. Supervisory Control and Data Acquisition (SCADA) systems monitor and control critical infrastructure such as the smart grid. As SCADA systems become increasingly interconnected and adopt more and more cyber-enabled components, the risks of cyber attacks become a major concern. Due to their decentralized organization, peer-to-peer (P2P) botnets are resilient to many existing takedown measures and can be exploited as an effective way to launch cyber attacks on SCADA systems. However, little work has been done to detect P2P botnets in SCADA systems, which carry traffic flows with characteristics significantly different from the Internet. In this paper, we design a P2P-botnet detection method for SCADA systems, leveraging built-in traffic monitoring capabilities of SCADA networking devices. The proposed method consists of two stages. In the first stage, we design a simple feature test to filter out non-P2P hosts, which significantly reduces the data volume for P2P-botnet identification. In the second stage, we jointly consider flow-based and connectivity-based features that effectively set apart bots from benign hosts. We propose to use unsupervised learning for P2P-botnet identification, which not only identifies known P2P botnets but also captures newly emerged ones. Our simulation results show that the proposed system achieves high detection rates with very few false positives. Furthermore, our evaluation shows that the proposed method can detect hosts running P2P SCADA applications that are infected by P2P bots. In this work we show that once a single peer-to-peer (P2P) bot is detected in a network, it may be possible to efficiently identify other members of the same botnet in the same network even before they exhibit any overtly malicious behavior. Detection is based on an analysis of connections made by the hosts in the network. It turns out that if bots select their peers randomly and independently (i.e. unstructured topology), any given pair of P2P bots in a network communicate with at least one mutual peer outside the network with a surprisingly high probability. This, along with the low probability of any other host communicating with this mutual peer, allows us to link local nodes within a P2P botnet together. We propose a simple method to identify potential members of an unstructured P2P botnet in a network starting from a known peer. We formulate the problem as a graph problem and mathematically analyze a solution using an iterative algorithm. The proposed scheme is simple and requires only flow records captured at network borders. We analyze the efficacy of the proposed scheme using real botnet data, including data obtained from both observing and crawling the Nugache botnet. Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observation that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system, BotSniffer, can capture this spatial-temporal correlation in network traffic and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated BotSniffer using many real-world network traces. The results show that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate. Advanced botnets adopt a peer-to-peer (P2P) infrastructure for more resilient command and control (C&C). Traditional detection techniques become less effective in identifying bots that communicate via a P2P structure. In this paper, we present PeerClean, a novel system that detects P2P botnets in real time using only high-level features extracted from C&C network flow traffic. PeerClean reliably distinguishes P2P bot-infected hosts from legitimate P2P hosts by jointly considering flow-level traffic statistics and network connection patterns. Instead of working on individual connections or hosts, PeerClean clusters hosts with similar flow traffic statistics into groups. It then extracts the collective and dynamic connection patterns of each group by leveraging a novel dynamic group behavior analysis. Comparing with the individual host-level connection patterns, the collective group patterns are more robust and differentiable. Multi-class classification models are then used to identify different types of bots based on the established patterns. To increase the detection probability, we further propose to train the model with average group behavior, but to explore the extreme group behavior for the detection. We evaluate PeerClean on real-world flow records from a campus network. Our evaluation shows that PeerClean is able to achieve high detection rates with few false positives. It is known that many Twitter users are bots, which are accounts controlled and sometimes created by computers. Twitter bots can send spam tweets, manipulate public opinion and be used for online fraud. Here we report the discovery, retrieval, and analysis of the Star Wars' botnet in Twitter, which consists of more than 350,000 bots tweeting random quotations exclusively from Star Wars novels. The botnet contains a single type of bot, showing exactly the same properties throughout the botnet. It is unusually large, many times larger than other available datasets. It provides a valuable source of ground truth for research on Twitter bots. We analysed and revealed rich details on how the botnet was designed and created. As of this writing, the Star Wars bots are still alive in Twitter. They have survived since their creation in 2013, despite the increasing efforts in recent years to detect and remove Twitter bots.We also reflect on the unconventional' way in which we discovered the Star Wars bots, and discuss the current problems and future challenges of Twitter bot detection. While botnets themselves provide a rich platform for financial gain for the botnet master, the use of the infected hosts as webservers can provide an additional botnet use. Botnet herders often use fast-flux DNS techniques to host unwanted or illegal content within a botnet. These techniques change the mapping of the domain name to different bots within the botnet with constant shifting, while the bots simply relay content back to a central server. This can give the attackers additional stepping stones to thwart takedown and can obscure their true origins. Evidence suggests that more attackers are adopting fast-flux techniques, but very little data has been gathered to discover what these botnets are being used for. To address this gap in understanding, we have been mining live traffic to discover new fast-flux domains and then tracking those botnets with active measurements for several months. We identified over 900 fast-flux domain names from early to mid 2008 and monitored their use across the Internet to discern fast-flux botnet behaviors. We found that the active lifetimes of fast-flux botnets vary from less than one day to months, domains that are used in fast-flux operations are often registered but dormant for months prior to activation, that these botnets are associated with a broad range of online fraud and crime activities, and that we can identify distinct botnets across multiple domain names. We support our findings through an in-depth examination of an Internet-scale data continuously collected for hundreds of domain names over several months. We propose Stegobot, a new generation botnet that communicates over probabilistically unobservable communication channels. It is designed to spread via social malware attacks and steal information from its victims. Unlike conventional botnets, Stegobot traffic does not introduce new communication endpoints between bots. Instead, it is based on a model of covert communication over a social-network overlay - bot to botmaster communication takes place along the edges of a social network. Further, bots use image steganography to hide the presence of communication within image sharing behavior of user interaction. We show that it is possible to design such a botnet even with a less than optimal routing mechanism such as restricted flooding. We analyzed a real-world dataset of image sharing between members of an online social network. Analysis of Stegobot's network throughput indicates that stealthy as it is, it is also functionally powerful - capable of channeling fair quantities of sensitive data from its victims to the botmaster at tens of megabytes every month. Peer-to-peer (P2P) botnets have recently been adopted by botmasters for their resiliency against take-down efforts. Besides being harder to take down, modern botnets tend to be stealthier in the way they perform malicious activities, making current detection approaches ineffective. In addition, the rapidly growing volume of network traffic calls for high scalability of detection systems. In this paper, we propose a novel scalable botnet detection system capable of detecting stealthy P2P botnets. Our system first identifies all hosts that are likely engaged in P2P communications. It then derives statistical fingerprints to profile P2P traffic and further distinguish between P2P botnet traffic and legitimate P2P traffic. The parallelized computation with bounded complexity makes scalability a built-in feature of our system. Extensive evaluation has demonstrated both high detection accuracy and great scalability of the proposed system. In this paper, we focus on characterizing spamming botnets by leveraging both spam payload and spam server traffic properties. Towards this goal, we developed a spam signature generation framework called AutoRE to detect botnet-based spam emails and botnet membership. AutoRE does not require pre-classified training data or white lists. Moreover, it outputs high quality regular expression signatures that can detect botnet spam with a low false positive rate. Using a three-month sample of emails from Hotmail, AutoRE successfully identified 7,721 botnet-based spam campaigns together with 340,050 unique botnet host IP addresses. Our in-depth analysis of the identified botnets revealed several interesting findings regarding the degree of email obfuscation, properties of botnet IP addresses, sending patterns, and their correlation with network scanning traffic. We believe these observations are useful information in the design of botnet detection schemes. Due to its popularity and open-source nature, Android is the mobile platform that has been targeted the most by malware that aim to steal personal information or to control the users’ devices. More specifically, mobile botnets are malware that allow an attacker to remotely control the victims’ devices through different channels like HTTP, thus creating malicious networks of bots. In this paper, we show how it is possible to effectively group mobile botnets families by analyzing the HTTP traffic they generate. To do so, we create malware clusters by looking at specific statistical information that are related to the HTTP traffic. This approach also allows us to extract signatures with which it is possible to precisely detect new malware that belong to the clustered families. Contrarily to x86 malware, we show that using fine-grained HTTP structural features do not increase detection performances. Finally, we point out how the HTTP information flow among mobile bots contains more information when compared to the one generated by desktop ones, allowing for a more precise detection of mobile threats. Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In this paper we considered one specific social botnet in Twitter to understand how it grows over time, how the content of tweets by the social botnet differ from regular users in the same dataset, and lastly, how the social botnet may have influenced the relevant discussions. Our analysis is based on a qualitative coding for approximately 3000 tweets in Arabic and English from the Syrian social bot that was active for 35 weeks on Twitter before it was shutdown. We find that the growth, behavior and content of this particular botnet did not specifically align with common conceptions of botnets. Further we identify interesting aspects of the botnet that distinguish it from regular users. The botnet, as one of the most formidable threats to cyber security, is often used to launch large-scale attack sabotage. How to accurately identify the botnet, especially to improve the performance of the detection model, is a key technical issue. In this paper, we propose a framework based on generative adversarial networks to augment botnet detection models (Bot-GAN). Moreover, we explore the performance of the proposed framework based on flows. The experimental results show that Bot-GAN is suitable for augmenting the original detection model. Compared with the original detection model, the proposed approach improves the detection performance, and decreases the false positive rate, which provides an effective method for improving the detection performance. In addition, it also retains the primary characteristics of the original detection model, which does not care about the network payload information, and has the ability to detect novel botnets and others using encryption or proprietary protocols. A key feature that distinguishes modern botnets from earlier counterparts is their increasing use of structured overlay topologies. This lets them carry out sophisticated coordinated activities while being resilient to churn, but it can also be used as a point of detection. In this work, we devise techniques to localize botnet members based on the unique communication patterns arising from their overlay topologies used for command and control. Experimental results on synthetic topologies embedded within Internet traffic traces from an ISP's backbone network indicate that our techniques (i) can localize the majority of bots with low false positive rate, and (ii) are resilient to incomplete visibility arising from partial deployment of monitoring systems and measurement inaccuracies from dynamics of background traffic.