text: This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building blocks of the multi-level detection architecture. The detectors identify malicious communication exploiting encrypted URL strings and domains generated by a Domain Generation Algorithm (DGA) which are frequently used in Command and Control (C&C), phishing, and click fraud. Surprisingly, very precise detectors can be built given only a limited amount of information extracted from a single proxy log. This way, the computational requirements of the detectors are kept low which allows for deployment on a wide range of security devices and without depending on traffic context such as DNS logs, Whois records, webpage content, etc. Results on several weeks of live traffic from 100+ companies having 350k+ hosts show correct detection with a precision exceeding 95 of malicious flows, 95 of malicious URLs and 90 of infected hosts. In addition, a comparison with a signature and rule-based solution shows that our system is able to detect significant amount of new threats.
  @cite_42: 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.
  @cite_1: We present a new kind of network perimeter monitoring strategy, which focuses on recognizing the infection and coordination dialog that occurs during a successful malware infection. BotHunter is an application designed to track the two-way communication flows between internal assets and external entities, developing an evidence trail of data exchanges that match a state-based infection sequence model. BotHunter consists of a correlation engine that is driven by three malware-focused network packet sensors, each charged with detecting specific stages of the malware infection process, including inbound scanning, exploit usage, egg downloading, outbound bot coordination dialog, and outbound attack propagation. The BotHunter correlator then ties together the dialog trail of inbound intrusion alarms with those outbound communication patterns that are highly indicative of successful local host infection. When a sequence of evidence is found to match BotHunter's infection dialog model, a consolidated report is produced to capture all the relevant events and event sources that played a role during the infection process. We refer to this analytical strategy of matching the dialog flows between internal assets and the broader Internet as dialog-based correlation, and contrast this strategy to other intrusion detection and alert correlation methods. We present our experimental results using BotHunter in both virtual and live testing environments, and discuss our Internet release of the BotHunter prototype. BotHunter is made available both for operational use and to help stimulate research in understanding the life cycle of malware infections.
  @cite_52: 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.
  @cite_24: 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.
  @cite_50: 
  @cite_31: New features of the PAYL anomalous payload detection sensor are demonstrated to accurately detect and generate signatures for zero-day worms. Experimental evidence demonstrates that site-specific packet content models are capable of detecting new worms with high accuracy in a collaborative security system. A new approach is proposed that correlates ingress egress payload alerts to identify the worm's initial propagation. The method also enables automatic signature generation that can be deployed immediately to network firewalls and content filters to proactively protect other hosts. We also propose a collaborative privacy-preserving security strategy whereby different hosts can exchange PAYL signatures to increase accuracy and mitigate against false positives. The important principle demonstrated is that correlating multiple alerts identifies true positives from the set of anomaly alerts and reduces incorrect decisions producing accurate mitigation.
summary: Payload packet inspection has been proposed in the PAYL system @cite_31 , where the fingerprints are represented as statistical distributions of bytes in a payload. The Provex system @cite_52 extends the inspection to encrypted data by maintaining a list of known decryption methods. In BotHunter @cite_1 , the anomalies found by PAYL are combined with anomaly detections from inbound and outbound scans and correlated with intrusion reports based on Snort rules @cite_50 . BotSniffer @cite_42 computes the anomaly scores from peaks in the number of entries for each destination IP and port pair aggregated over a longer time period. The anomaly detectors are reused in the BotMiner system @cite_24 to create clusters of similar host activities (so called A-plane). The A-plane is correlated with the clustering of communication patterns represented by the flow statistics (C-plane) to produce the final report. Despite their effectiveness, the packet analysis NIDS systems cannot always be deployed since the increasing network bandwidth increases the rates of data processing and packet inspection may violate privacy protection imposed by internal local network policies. The proposed system does not use packet inspection or clustering which allows it to achieve fast processing speeds necessary for online deployment.