We present a novel network-level behavioral malware clustering system. focus on analyzing the structural similarities among malicious HTTP traffic traces generated by executing HTTP-based malware. Our work is motivated need to provide quality input algorithms that automatically generate network signatures. Accordingly, we define similarity metrics and develop our system so resulting clusters can yield high-quality signatures.We implemented proof-of-concept version of performed experiments...

Unsupervised or unlabeled learning approaches for network anomaly detection have been recently proposed. In particular, recent work on focused high speed classification based simple payload statistics. For example, PAYL, an IDS, measures the occurrence frequency in of n-grams. A model normal traffic is then constructed according to this description packets' content. It has demonstrated that detectors statistics can be "evaded" by mimicry attacks using byte substitution and padding...

Several syntactic-based automatic worm signature generators, e.g., Polygraph, have recently been proposed. These systems typically assume that a set of suspicious flows are provided by flow classifier, honeynet or an intrusion detection system, often introduces "noise" due to difficulties and imprecision inflow classification. The algorithms for extracting the signatures from data designed cope with noise. It has reported these can handle fairly high noise level, 80% Polygraph. In this...

In this work, we propose Malware Collection Booster (McBoost), a fast statistical malware detection tool that is intended to improve the scalability of existing collection and analysis approaches. Given large binaries may contain both hitherto unknown benign executables, McBoost reduces overall time by classifying filtering out least suspicious passing only most ones detailed binary process for signature extraction.The framework consists classifier specialized in detecting whether an...

In this paper, we propose Segugio, a novel defense system that allows for efficiently tracking the occurrence of new malware-control domain names in very large ISP networks. Segugio passively monitors DNS traffic to build machine-domain bipartite graph representing who is querying what. After labelling nodes query behavior are known be either benign or malware-related, approach accurately detect previously unknown domains. We implemented proof-of-concept version and deployed it networks...

In this paper we propose a novel, passive approach for detecting and tracking malicious flux service networks. Our detection system is based on analysis of recursive DNS (RDNS) traffic traces collected from multiple large Contrary to previous work, our not limited the suspicious domain names extracted spam emails or precompiled blacklists. Instead, able detect networks in-the-wild, i.e., as they are accessed by users who fall victims content advertised through blog spam, instant messaging...

Peer-to-peer (P2P) botnets have recently been adopted by botmasters for their resiliency to take-down efforts. Besides being harder take down, modern tend be stealthier in the way they perform malicious activities, making current detection approaches, including, ineffective. In this paper, we propose a novel botnet system that is able identify stealthy P2P botnets, even when activities may not observable. First, our identifies all hosts are likely engaged communications. Then, derive...

In this paper, we present FluxBuster, a novel passive DNS traffic analysis system for detecting and tracking malicious flux networks. FluxBuster applies large-scale monitoring of traces generated by recursive (RDNS) servers located in hundreds different networks scattered across several geographical locations. Unlike most previous work, our detection approach is not limited to the suspicious domain names extracted from spam emails or precompiled blacklists. Instead, able detect service...

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 tend be stealthier in the way they perform malicious activities, making current detection approaches ineffective. In addition, rapidly growing volume of network traffic calls high scalability systems. this paper, we propose a novel scalable botnet system capable detecting stealthy P2P botnets. Our first identifies all hosts that are...

Search engine optimization (SEO) techniques are often abused to promote websites among search results. This is a practice known as blackhat SEO. In this paper we tackle newly emerging and especially aggressive class of SEO, namely poisoning. Unlike other SEO techniques, which typically attempt website's ranking only under limited set keywords relevant the content, poisoning disregard any term relevance constraint employed poison popular with sole purpose diverting large numbers users...

Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest lot effort mitigate ads from their networks. This drives adversaries look alternative methods deploy malvertising. In this paper, we show browser extensions use monetization strategy often facilitate deployment Moreover, while some simply serve networks support...

Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done understand their ability withstand adversarial attacks. We design and evaluate two novel graph attacks against state-of-the-art network-level, graph-based detection system. Our highlights areas machine learning that have not yet addressed, specifically: clustering techniques, global feature space where realistic attackers without perfect knowledge must accounted for (by the...

Being able to enumerate potentially vulnerable IoT devices across the Internet is important, because it allows for assessing global risks and enables network operators check hygiene of their own networks. To this end, in paper we propose IoTFinder, a system efficient, large-scale <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive</inf> identification devices. Specifically, leverage distributed passive DNS data collection, develop machine...

Devices infected with malicious software typically form botnet armies under the influence of one or more command and control (C&C) servers. The problem reached such levels where federal law enforcement agencies have to step in take actions against botnets by disrupting (or "taking down") their C&Cs, thus illicit operations. Lately, private companies started independently action armies, primarily focusing on DNS-based C&Cs. While well-intentioned, C&C takedown methodology is most cases...