Christian Rossow

SoK: P2PWNED - Modeling and Evaluating the Resilience of Peer-to-Peer Botnets

Centralized botnets are easy targets for takedown efforts by computer security researchers and law enforcement. Thus, botnet controllers have sought new ways to harden the infrastructures of their botnets. In order to meet this objective, some botnet operators have (re)designed their botnets to use Peer-to-Peer (P2P) infrastructures. Many P2P botnets are far more resilient to takedown attempts than centralized botnets, because they have no single points of failure. However, P2P botnets are subject to unique classes of attacks, such as node enumeration and poisoning. In this paper, we introduce a formal graph model to capture the intrinsic properties and fundamental vulnerabilities of P2P botnets. We apply our model to current P2P botnets to assess their resilience against attacks. We provide assessments on the sizes of all eleven active P2P botnets, showing that some P2P botnet families contain over a million bots. In addition, we have prototyped several mitigation strategies to measure the resilience of existing P2P botnets. We believe that the results from our analysis can be used to assist security researchers in evaluating mitigation strategies against current and future P2P botnets.

Prudent Practices for Designing Malware Experiments: Status Quo and Outlook

Malware researchers rely on the observation of malicious code in execution to collect datasets for a wide array of experiments, including generation of detection models, study of longitudinal behavior, and validation of prior research. For such research to reflect prudent science, the work needs to address a number of concerns relating to the correct and representative use of the datasets, presentation of methodology in a fashion sufficiently transparent to enable reproducibility, and due consideration of the need not to harm others. In this paper we study the methodological rigor and prudence in 36 academic publications from 2006-2011 that rely on malware execution. 40% of these papers appeared in the 6 highest-ranked academic security conferences. We find frequent shortcomings, including problematic assumptions regarding the use of execution-driven datasets (25% of the papers), absence of description of security precautions taken during experiments (71% of the articles), and oftentimes insufficient description of the experimental setup. Deficiencies occur in top-tier venues and elsewhere alike, highlighting a need for the community to improve its handling of malware datasets. In the hope of aiding authors, reviewers, and readers, we frame guidelines regarding transparency, realism, correctness, and safety for collecting and using malware datasets.

On Botnets That Use DNS for Command and Control

We discovered and reverse engineered Feederbot, a botnet that uses DNS as carrier for its command and control. Using k-Means clustering and a Euclidean Distance based classifier, we correctly classified more than 14m DNS transactions of 42,143 malware samples concerning DNS-C&C usage, revealing another bot family with DNS C&C. In addition, we correctly detected DNS C&C in mixed office workstation network traffic.

Sandnet: network traffic analysis of malicious software

Dynamic analysis of malware is widely used to obtain a better understanding of unknown software. While existing systems mainly focus on host-level activities of malware and limit the analysis period to a few minutes, we concentrate on the network behavior of malware over longer periods. We provide a comprehensive overview of typical malware network behavior by discussing the results that we obtained during the analysis of more than 100,000 malware samples. The resulting network behavior was dissected in our new analysis environment called Sandnet that complements existing systems by focusing on network traffic analysis. Our in-depth analysis of the two protocols that are most popular among malware authors, DNS and HTTP, helps to understand and characterize the usage of these prevalent protocols.

Highly resilient peer-to-peer botnets are here: An analysis of Gameover Zeus

Zeus is a family of credential-stealing trojans which originally appeared in 2007. The first two variants of Zeus are based on centralized command servers. These command servers are now routinely tracked and blocked by the security community. In an apparent effort to withstand these routine countermeasures, the second version of Zeus was forked into a peer-to-peer variant in September 2011. Compared to earlier versions of Zeus, this peer-to-peer variant is fundamentally more difficult to disable. Through a detailed analysis of this new Zeus variant, we demonstrate the high resilience of state of the art peer-to-peer botnets in general, and of peer-to-peer Zeus in particular.

Cross-Architecture Bug Search in Binary Executables

With the general availability of closed-source software for various CPU architectures, there is a need to identify security-critical vulnerabilities at the binary level to perform a vulnerability assessment. Unfortunately, existing bug finding methods fall short in that they i) require source code, ii) only work on a single architecture (typically x86), or iii) rely on dynamic analysis, which is inherently difficult for embedded devices. In this paper, we propose a system to derive bug signatures for known bugs. We then use these signatures to find bugs in binaries that have been deployed on different CPU architectures (e.g., x86 vs. MIPS). The variety of CPU architectures imposes many challenges, such as the incomparability of instruction set architectures between the CPU models. We solve this by first translating the binary code to an intermediate representation, resulting in assignment formulas with input and output variables. We then sample concrete inputs to observe the I/O behavior of basic blocks, which grasps their semantics. Finally, we use the I/O behavior to find code parts that behave similarly to the bug signature, effectively revealing code parts that contain the bug. We have designed and implemented a tool for cross architecture bug search in executables. Our prototype currently supports three instruction set architectures (x86, ARM, and MIPS) and can find vulnerabilities in buggy binary code for any of these architectures. We show that we can find Heart bleed vulnerabilities, regardless of the underlying software instruction set. Similarly, we apply our method to find backdoors in closed source firmware images of MIPS- and ARM-based routers.

CoCoSpot: Clustering and recognizing botnet command and control channels using traffic analysis

We present CoCoSpot, a novel approach to recognize botnet command and control channels solely based on traffic analysis features, namely carrier protocol distinction, message length sequences and encoding differences. Thus, CoCoSpot can deal with obfuscated and encrypted C&C protocols and complements current methods to fingerprint and recognize botnet C&C channels. Using average-linkage hierarchical clustering of labeled C&C flows, we show that for more than 20 recent botnets and over 87,000 C&C flows, CoCoSpot can recognize more than 88% of the C&C flows at a false positive rate below 0.1%.

Paint It Black: Evaluating the Effectiveness of Malware Blacklists

Blacklists are commonly used to protect computer systems against the tremendous number of malware threats. These lists include abusive hosts such as malware sites or botnet Command & Control and dropzone servers to raise alerts if suspicious hosts are contacted. Up to now, though, little is known about the effectiveness of malware blacklists.

ProVeX: detecting botnets with encrypted command and control channels

Abstract. Botmasters increasingly encrypt command-and-control (C&C) communication to evade existing intrusion detection systems. Our detailed C&C traffic analysis shows that at least ten prevalent malware families avoid well-known C&C carrier protocols, such as IRC and HTTP. Six of these families - e.g., Zeus P2P, Pramro, Virut, and Sality - do not exhibit any characteristic n-gram that could serve as payload-based signature in an IDS. Given knowledge of the C&C encryption algorithms, we detect these evasive C&C protocols by decrypting any packet captured on the network. In order to test if the decryption results in messages that stem from malware, we propose ProVex, a system that automatically derives probabilistic vectorized signatures. ProVex learns characteristic values for fields in the C&C protocol by evaluating byte probabilities in C&C input traces used for training. This way, we identify the syntax of C&C messages without the need to manually specify C&C protocol semantics, purely based on network traffic. Our evaluation shows that ProVex can detect all studied malware families, most of which are not detectable with traditional means. Despite its naive approach to decrypt all traffic, we show that ProVex scales up to multiple Gbit/s line speed networks.

AmpPot: Monitoring and Defending Against Amplification DDoS Attacks

The recent amplification DDoS attacks have swamped victims with huge loads of undesired traffic, sometimes even exceeding hundreds of Gbps attack bandwidth. We analyze these amplification attacks in more detail. First, we inspect the reconnaissance step, i.e., how both researchers and attackers scan for amplifiers that are open for abuse. Second, we design AmpPot, a novel honeypot that tracks amplification attacks. We deploy 21 honeypots to reveal previously-undocumented insights about the attacks. We find that the vast majority of attacks are short-lived and most victims are attacked only once. Furthermore, 96i¾?% of the attacks stem from single sources, which is also confirmed by our detailed analysis of four popular Linux-based DDoS botnets.