Detecting Spam Zombies by Monitoring Outgoing Messages

Compromised machines are one of the key security threats on the Internet; they are often used to launch various security attacks such as DDoS, spamming, and identity theft. In this thesis we address this issue by investigating effective solutions to automatically identify compromised machines in a network. Given that spamming provides a key economic incentive for attackers to recruit the large number of compromised machines, we focus on the subset of compromised machines that are involved in the spamming activities, commonly known as spam zombies. We develop an effective spam zombie detection system named SPOT by monitoring outgoing messages of a network. SPOT is designed based on a powerful statistical tool called Sequential Probability Ratio Test, which has bounded false positive and false negative error rates. Our evaluation studies based on a two-month email trace collected in a large U.S. campus network show that SPOT is an effective and efficient system in automatically detecting compromised machines in a network. For example, among the 440 internal IP addresses observed in the email trace, SPOT identifies 132 of them as being associated with compromised machines. Out of the 132 IP addresses identified by SPOT, 126 can be either independently confirmed (110) or highly likely (16) to be compromised. Moreover, only 7 internal IP addresses associated with compromised machines in the trace are missed by SPOT.