Peer-to-peer and other decentralized, distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack, a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system, the malicious user is able to “out vote” the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, anovelprotocolfor limiting the corruptive influences of sybil attacks. Our protocol is based on the “social network ” among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately-small “cut ” in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create. We show the effectiveness of SybilGuard both analytically and experimentally.

Decentralized distributed systems such as peer-to-peer systems are particularly vulnerable to sybil attacks, where a malicious user pretends to have multiple identities (called sybil nodes). Without a trusted central authority, defending against sybil attacks is quite challenging. Among the small number of decentralized approaches, our recent SybilGuard protocol [43] leverages a key insight on social networks to bound the number of sybil nodes accepted. Although its direction is promising, SybilGuard can allow a large number of sybil nodes to be accepted. Furthermore, SybilGuard assumes that social networks are fast mixing, which has never been confirmed in the real world. This paper presents the novel SybilLimit protocol that leverages the same insight as SybilGuard but offers dramatically improved and near-optimal guarantees. The number of sybil nodes accepted is reduced by a factor of Θ ( √ n), or around 200 times in our experiments for a million-node system. We further prove that SybilLimit’s guarantee is at most a log n factor away from optimal, when considering approaches based on fast-mixing social networks. Finally, based on three large-scale real-world social networks, we provide the first evidence that real-world social networks are indeed fast mixing. This validates the fundamental assumption behind SybilLimit’s and SybilGuard’s approach. 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.

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

In a BGP prefix hijacking event, a router originates a route to a prefix, but does not provide data delivery to the actual prefix. Prefix hijacking events have been widely reported and are a serious problem in the Internet. This paper presents a new Prefix Hijack Alert System (PHAS). PHAS is a real-time notification system that alerts prefix owners when their BGP origin changes. By providing reliable and timely notification of origin AS changes, PHAS allows prefix owners to quickly and easily detect prefix hijacking events and take prompt action to address the problem. We illustrate the effectiveness of PHAS and evaluate its overhead using BGP logs collected from RouteViews. PHAS is light-weight, easy to implement, and readily deployable. In addition to protecting against false BGP origins, the PHAS concept can be extended to detect prefix hijacking events that involve announcing more specific prefixes or modifying the last hop in the path.

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.

This paper presents the design, implementation, analysis, and experimental evaluation of speak-up, a defense against applicationlevel distributed denial-of-service (DDoS), in which attackers cripple a server by sending legitimate-looking requests that consume computational resources (e.g., CPU cycles, disk). With speak-up, a victimized server encourages all clients, resources permitting, to automatically send higher volumes of traffic. We suppose that attackers are already using most of their upload bandwidth so cannot react to the encouragement. Good clients, however, have spare upload bandwidth and will react to the encouragement with drastically higher volumes of traffic. The intended outcome of this traffic inflation is that the good clients crowd out the bad ones, thereby capturing a much larger fraction of the server’s resources than before. We experiment under various conditions and find that speak-up causes the server to spend resources on a group of clients in rough proportion to their aggregate upload bandwidth. This result makes the defense viable and effective for a class of real attacks.

In this paper, we focus on characterizing spamming botnets by leveraging both spam payload and spam server traffic properties. Towards this goal, we developed a spam signature generation framework called AutoRE to detect botnet-based spam emails and botnet membership. AutoRE does not require pre-classified training data or white lists. Moreover, it outputs high quality regular expression signatures that can detect botnet spam with a low false positive rate. Using a three-month sample of emails from Hotmail, AutoRE successfully identified 7,721 botnet-based spam campaigns together with 340,050 unique botnet host IP addresses. Our in-depth analysis of the identified botnets revealed several interesting findings regarding the degree of email obfuscation, properties of botnet IP addresses, sending patterns, and their correlation with network scanning traffic. We believe these observations are useful information in the design of botnet detection schemes.

... machines—are often used for nefarious activities (e.g., spam, click fraud, denial-of-service attacks, etc.). Identifying members of botnets could help stem these attacks, but passively detecting botnet membership (i.e., without disrupting the operation of the botnet) proves to be difficult. This paper studies the effectiveness of monitoring lookups to a DNS-based blackhole list (DNSBL) to expose botnet membership. We perform counter-intelligence based on the insight that botmasters themselves perform DNSBL lookups to determine whether their spamming bots are blacklisted. Using heuristics to identify which DNSBL lookups are perpetrated by a botmaster performing such reconnaissance, we are able to compile a list of likely bots. This paper studies the prevalence of DNSBL reconnaissance observed at a mirror of a well-known blacklist for a 45day period, identifies the means by which botmasters are performing reconnaissance, and suggests the possibility of using counter-intelligence to discover likely bots. We find that bots are performing reconnaissance on behalf of other bots. Based on this finding, we suggest counterintelligence techniques that may be useful for early bot detection.

Spam filters often use the reputation of an IP address (or IP address range) to classify email senders. This approach worked well when most spam originated from senders with fixed IP addresses, but spam today is also sent from IP addresses for which blacklist maintainers have outdated or inaccurate information (or no information at all). Spam campaigns also involve many senders, reducing the amount of spam any particular IP address sends to a single domain; this method allows spammers to stay “under the radar”. The dynamism of any particular IP address begs for blacklisting techniques that automatically adapt as the senders of spam change. This paper presents SpamTracker, a spam filtering system that uses a new technique called behavioral blacklisting to classify email senders based on their sending behavior rather than their identity. Spammers cannot evade SpamTracker merely by using “fresh” IP addresses because blacklisting decisions are based on sending patterns, which tend to remain more invariant. SpamTracker uses fast clustering algorithms that react quickly to changes in sending patterns. We evaluate SpamTracker’s ability to classify spammers using email logs for over 115 email domains; we find that SpamTracker can correctly classify many spammers missed by current filtering techniques. Although our current datasets prevent us from confirming SpamTracker’s ability to completely distinguish spammers from legitimate senders, our evaluation shows that Spam-Tracker can identify a significant fraction of spammers that current IP-based blacklists miss. SpamTracker’s ability to identify spammers before existing blacklists suggests that it can be used in conjunction with existing techniques (e.g., as an input to greylisting). SpamTracker is inherently distributed and can be easily replicated; incorporating it into existing email filtering infrastructures requires only small modifications to mail server configurations.