Rigorously characterizing the statistical properties of cyber attacks is an important problem. In this paper, we propose the first statistical framework for rigorously analyzing honeypot-captured cyber attack data. The framework is built on the novel concept of stochastic cyber attack process , a new kind of mathematical objects for describing cyber attacks. To demonstrate use of the framework, we apply it to analyze a low-interaction honeypot dataset, while noting that the framework can be equally applied to analyze high-interaction honeypot data that contains richer information about the attacks. The case study finds, for the first time, that Long-Range Dependence (LRD) is exhibited by honeypot-captured cyber attacks. The case study confirms that by exploiting the statistical properties (LRD in this case), it is feasible to predict cyber attacks (at least in terms of attack rate) with good accuracy. This kind of prediction capability would provide sufficient early-warning time for defenders to adjust their defense configurations or resource allocations. The idea of "gray-box" (rather than "black-box") prediction is central to the utility of the statistical framework, and represents a significant step towards ultimately understanding (the degree of) the predictability of cyber attacks.
Putting data-driven analysis of cyber attacks into a broader context, we note that there have been studies on characterizing blackhole-collected traffic data (e.g., @cite_4 @cite_19 ) or one-way traffic in live networks @cite_23 . Still, there are no advanced statistical framework for analyzing such blackhole or one-way traffic data. More specifically, these studies differ from ours in (i) honeypot-captured cyber attack data includes two-way communications, whereas blackhole-collected data mainly corresponds to one-way communications; (ii) we rigorously explore statistical properties such as LRD, whereas their studies do not pursue such rigorous statistical analysis. Nevertheless, it is possible that our analysis framework can be adapted to analyze blackhole data.
@cite_19 @cite_4 @cite_23
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We investigate the emergence of the exploit-as-a-service model for driveby browser compromise. In this regime, attackers pay for an exploit kit or service to do the "dirty work" of exploiting a victim's browser, decoupling the complexities of browser and plugin vulnerabilities from the challenges of generating traffic to a website under the attacker's control. Upon a successful exploit, these kits load and execute a binary provided by the attacker, effectively transferring control of a victim's machine to the attacker. In order to understand the impact of the exploit-as-a-service paradigm on the malware ecosystem, we perform a detailed analysis of the prevalence of exploit kits, the families of malware installed upon a successful exploit, and the volume of traffic that malicious web sites receive. To carry out this study, we analyze 77,000 malicious URLs received from Google Safe Browsing, along with a crowd-sourced feed of blacklisted URLs known to direct to exploit kits. These URLs led to over 10,000 distinct binaries, which we ran in a contained environment. Our results show that many of the most prominent families of malware now propagate through driveby downloads--32 families in all. Their activities are supported by a handful of exploit kits, with Blackhole accounting for 29 of all malicious URLs in our data, followed in popularity by Incognito. We use DNS traffic from real networks to provide a unique perspective on the popularity of malware families based on the frequency that their binaries are installed by drivebys, as well as the lifetime and popularity of domains funneling users to exploits. Communication using VANETs is commonly seen as the next milestone for improving traffic safety. Vehicles will be enabled to exchange any kind of information that helps to detect and mitigate dangerous situations. Security research in the past years has shown that VANETs are endangered by a plethora of severe security risk. Subject of this work is the modeling of attackers that target active safety applications in VANETs. Through a risk analysis, this work identifies assets, threats and potential attacks in inter-vehicle communication. The risk analysis shows that the most serious threat arises from a quasi-stationary (road-side) attacker that distributed forged warning messages. This attacker is discussed more deeply. We show the degrees of freedom that are available for position forging and find thereby two attacks that demand attention: single position forging having low effort compared to sophisticated movement path forging having a potentially high influence on road traffic safety. In network intrusion detection, anomaly-based approaches in particular suffer from accurate evaluation, comparison, and deployment which originates from the scarcity of adequate datasets. Many such datasets are internal and cannot be shared due to privacy issues, others are heavily anonymized and do not reflect current trends, or they lack certain statistical characteristics. These deficiencies are primarily the reasons why a perfect dataset is yet to exist. Thus, researchers must resort to datasets that are often suboptimal. As network behaviors and patterns change and intrusions evolve, it has very much become necessary to move away from static and one-time datasets toward more dynamically generated datasets which not only reflect the traffic compositions and intrusions of that time, but are also modifiable, extensible, and reproducible. In this paper, a systematic approach to generate the required datasets is introduced to address this need. The underlying notion is based on the concept of profiles which contain detailed descriptions of intrusions and abstract distribution models for applications, protocols, or lower level network entities. Real traces are analyzed to create profiles for agents that generate real traffic for HTTP, SMTP, SSH, IMAP, POP3, and FTP. In this regard, a set of guidelines is established to outline valid datasets, which set the basis for generating profiles. These guidelines are vital for the effectiveness of the dataset in terms of realism, evaluation capabilities, total capture, completeness, and malicious activity. The profiles are then employed in an experiment to generate the desirable dataset in a testbed environment. Various multi-stage attacks scenarios were subsequently carried out to supply the anomalous portion of the dataset. The intent for this dataset is to assist various researchers in acquiring datasets of this kind for testing, evaluation, and comparison purposes, through sharing the generated datasets and profiles. We consider the analysis of network traffic data for identifying highly agile DNS patterns which are widely considered indicative for cybercrime. In contrast to related approaches, our methodology is capable of explicitly distinguishing between the individual, inherent agility of benign Internet services and criminal sites. Although some benign services use a large number of addresses, they are confined to a subset of IP addresses, due to operational requirements and contractual agreements with certain Content Distribution Networks. We discuss DNSMap, a system which analyzes observed DNS traffic, and continuously learns which FQDNs are hosted on which IP addresses. Any significant changes over time are mapped to bipartite graphs, which are then further pruned for cybercrime activity. Graph analysis enables the detection of transitive relations between FQDNs and IPs, and reveals clusters of malicious FQDNs and IP addresses hosting them. We developed a prototype system which is designed for realtime analysis, requires no costly classifier retraining, and no excessive whitelisting. We evaluate our system using large data sets from an ISP with several 100,000 customers, and demonstrate that even moderately agile criminal sites can be detected reliably and almost immediately.