Modeling and Defending against Adaptive BitTorrent Worms in Peer-to-Peer Networks

Abstract

BitTorrent (BT) is one of the most common Peer-to-Peer (P2P) file sharing protocols. Rather than downloading a file from a single source, the protocol allows users to join a swarm of peers to download and upload from each other simultaneously. Worms exploiting information from BT servers or trackers can cause serious damage to participating peers, which unfortunately has been neglected previously. In this article, we first present a new worm, called Adaptive BitTorrent worm (A-BT worm), which finds new victims and propagates sending forged requests to trackers. To reduce its abnormal behavior, the worm estimates the ratio of infected peers and adaptively adjusts its propagation speed. We then build a hybrid model to precisely characterize the propagation behavior of the worm. We also propose a statistical method to automatically detect the worm from the tracker by estimating the variance of the time intervals of requests. To slow down the worm propagation, we design a safe strategy in which the tracker returns secured peers when receives a request. Finally, we evaluate the accuracy of the hybrid model, and the effectiveness of our detection method and containment strategy through simulations.