Privacy-preserving payload-based correlation for accurate malicious traffic detection

Abstract

With the increased use of botnets and other techniques to obfuscate attackers' command-and-control centers, Distributed Intrusion Detection Systems (DIDS) that focus on attack source IP addresses or other header information can only portray a limited view of distributed scans and attacks. Packet payload sharing techniques hold far more promise, as they can convey exploit vectors and/or malcode used upon successful exploit of a target system, irrespective of obfuscated source addresses. However, payload sharing has had minimal success due to regulatory or business-based privacy concerns of transmitting raw or even sanitized payloads. The currently accepted form of content exchange has been limited to the exchange of known-suspicious content, e.g., packets captured by honeypots; however, signature generation assumes that each site receives enough traffic in order to correlate a meaningful set of payloads from which common content can be derived, and places fundamental and computationally stressful requirements on signature generators that may miss particularly stealthy or carefully-crafted polymorphic malcode.Instead, we propose a new approach to enable the sharing of suspicious payloads via privacy-preserving technologies. We detail the work we have done with two example payload anomaly detectors, PAYL and Anagram, to support generalized payload correlation and signature generation without releasing identifiable payload data and without relying on single-site signature generation. We present preliminary results of our approaches and suggest how such deployments may practically be used for not only cross-site, but also cross-domain alert sharing and its implications for profiling threats.