Abstract. Intruders on the Internet often prefer to launch network intrusions indirectly, i.e., using a chain of hosts on the Internet as relay machines using protocols such as Telnet or SSH. This type of attack is called a stepping-stone attack. In this paper, we propose and analyze algorithms for stepping-stone detection using ideas from Computational Learning Theory and the analysis of random walks. Our results are the first to achieve provable (polynomial) upper bounds on the number of packets needed to confidently detect and identify encrypted steppingstone streams with proven guarantees on the probability of falsely accusing non-attacking pairs. Moreover, our methods and analysis rely on mild assumptions, especially in comparison to previous work. We also examine the consequences when the attacker inserts chaff into the stepping-stone traffic, and give bounds on the amount of chaff that an attacker would have to send to evade detection. Our results are based on a new approach which can detect correlation of streams at a fine-grained level. Our approach may also apply to more generalized traffic analysis domains, such as anonymous communication. Key words: Network intrusion detection. Evasion. Stepping stones. Interactive sessions. Random walks.
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