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.

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. 1

Botnets, networks of malware-infected machines that are controlled by an adversary, are the root cause of a large number of security problems on the Internet. A particularly sophisticated and insidious type of bot is Torpig, a malware program that is designed to harvest sensitive information (such as bank account and credit card data) from its victims. In this paper, we report on our efforts to take control of the Torpig botnet and study its operations for a period of ten days. During this time, we observed more than 180 thousand infections and recorded almost 70 GB of data that the bots collected. While botnets have been “hijacked ” and studied previously, the Torpig botnet exhibits certain properties that make the analysis of the data particularly interesting. First, it is possible (with reasonable accuracy) to identify unique bot infections and relate that number to the more than 1.2 million IP addresses that contacted our command and control server. Second, the Torpig botnet is large, targets a variety of applications, and gathers a rich and diverse set of data from the infected victims. This data provides a new understanding of the type and amount of personal information that is stolen by botnets. 1.

In this paper we present Botlab, a platform that continually monitors and analyzes the behavior of spamoriented botnets. Botlab gathers multiple real-time streams of information about botnets taken from distinct perspectives. By combining and analyzing these streams, Botlab can produce accurate, timely, and comprehensive data about spam botnet behavior. Our prototype system integrates information about spam arriving at the University of Washington, outgoing spam generated by captive botnet nodes, and information gleaned from DNS about URLs found within these spam messages. We describe the design and implementation of Botlab, including the challenges we had to overcome, such as preventing captive nodes from causing harm or thwarting virtual machine detection. Next, we present the results of a detailed measurement study of the behavior of the most active spam botnets. We find that six botnets are responsible for 79 % of spam messages arriving at the UW campus. Finally, we present defensive tools that take advantage of the Botlab platform to improve spam filtering and protect users from harmful web sites advertised within botnet-generated spam.

We propose a taxonomy of botnet structures, based on their utility to the botmaster. We propose key metrics to measure their utility for various activities (e.g., spam, ddos). Using the performance metrics, we consider the ability of different response techniques to degrade or disrupt botnets. In particular, our models show that for scale free botnets, targeted responses are particularly effective. Further, botmasters ’ efforts to improve the robustness of scale free networks comes at a cost of diminished transitivity. Botmasters do not appear to have any structural solutions to this problem in scale free networks. We also show that random graph botnets (e.g., those using P2P formations) are highly resistant to both random and targeted responses. We evaluate the impact of responses on different topologies using simulation. We also perform some novel measurements of a P2P network to demonstrate the utility of our proposed metrics. Our analysis shows how botnets may be classified according to structure, and given rank or priority using our proposed metrics. This may help direct responses, and suggests which general remediation strategies are more likely to succeed. 1

We develop new techniques to map botnet membership using traces of spam email. To group bots into botnets we look for multiple bots participating in the same spam email campaign. We have applied our technique against a trace of spam email from Hotmail Web mail services. In this trace, we have successfully identified hundreds of botnets. We present new findings about botnet sizes and behavior while also confirming other researcher’s observations derived by different methods [1, 15]. 1

Abstract. A botnet is a collection of bots, each generally running on a compromised system and responding to commands over a “commandand-control” overlay network. We investigate observable differences in the behavior of bots and benign programs, focusing on the way that bots respond to data received over the network. Our experimental platform monitors execution of an arbitrary Win32 binary, considering data received over the network to be tainted, applying library-call-level taint propagation, and checking for tainted arguments to selected system calls. As a way of further distinguishing locally-initiated from remotely-initiated actions, we capture and propagate “cleanliness ” of local user input (as received via the keyboard or mouse). Testing indicates behavioral separation of major bot families (agobot, DSNXbot, evilbot, G-SySbot, sdbot, Spybot) from benign programs with low error rate. 1

Abstract. Stealthy malware, such as botnets and spyware, are hard to detect because their activities are subtle and do not disrupt the network, in contrast to DoS attacks and aggressive worms. Stealthy malware, however, does communicate to exfiltrate data to the attacker, to receive the attacker’s commands, or to carry out those commands. Moreover, since malware rarely infiltrates only a single host in a large enterprise, these communications should emerge from multiple hosts within coarse temporal proximity to one another. In this paper, we describe a system called TĀMD (pronounced “tamed”) with which an enterprise can identify candidate groups of infected computers within its network. TĀMD accomplishes this by finding new communication “aggregates ” involving multiple internal hosts, i.e., communication flows that share common characteristics. We describe characteristics for defining aggregates—including flows that communicate with the same external network, that share similar payload, and/or that involve internal hosts with similar software platforms—and justify their use in finding infected hosts. We also detail efficient algorithms employed by TĀMD for identifying such aggregates, and demonstrate a particular configuration of TĀMD that identifies new infections for multiple bot and spyware examples, within traces of traffic recorded at the edge of a university network. This is achieved even when the number of infected hosts comprise only about 0.0097 % of all internal hosts in the network. 1

Abstract. A botnet is a network of compromised hosts that is under the control of a single, malicious entity, often called the botmaster. We present a system that aims to detect bots, independent of any prior information about the command and control channels or propagation vectors, and without requiring multiple infections for correlation. Our system relies on detection models that target the characteristic fact that every bot receives commands from the botmaster to which it responds in a specific way. These detection models are generated automatically from network traffic traces recorded from actual bot instances. We have implemented the proposed approach and demonstrate that it can extract effective detection models for a variety of different bot families. These models are precise in describing the activity of bots and raise very few false positives. 1

A Redirection Botnet (RBnet) is a vast collection of compromised computers (called bots) used as a redirection/proxy infrastructure and under the control of a botmaster. We present the design, implementation and evaluation of a system called Redirection Botnet Seeker (RB-Seeker) for automatic detection of RBnets by utilizing three cooperating subsystems. Two of the subsystems are used to generate a database of domains participating in redirection: one detects redirection bots by following links embedded in spam emails, and the other detects redirection behavior based on network traces at a large university edge router using sequential hypothesis testing. The database of redirection domains generated by these two subsystems is fed into the final subsystem, which then performs DNS query probing on the domains over time. Based on certain behavioral attributes extracted from the DNS queries, the final subsystem makes use of a 2-tier detection strategy utilizing hyperplane decision functions. This allows it to quickly identify aggressive RBnets with a low false-positive rate (< 0.008%), while also accurately detecting stealthy RBnets (i.e., those mimicking valid DNS behavior, such as CDNs) by monitoring their behavior over time. Using DNS behavior as a means of detecting RBnets, RB-Seeker is impervious to the botmaster’s choice of Command-and-Control (C&C) channel (i.e., how the botmaster communicates and controls the bots) or use of encryption. 1