An Architecture for an Email Worm Prevention System

Abstract

Email worms comprise the largest portion of Internet worms today. Previous research has shown that they are an effective vehicle to deliver malicious code to a large group of users. These worms spread rapidly using the email infrastructure, causing significant financial damage, network congestion, and privacy invasion. We present a dynamic architecture to proactively defend a protected domain against email worms. This architecture integrates concepts from the areas of Markov decision processes, Rabin fingerprinting and honeypots to inspect, detect, and quarantine unknown email worms in a timely manner. We also present the results of several simulation experiments to evaluate the effectiveness of the architecture under different environment conditions