Proceedings of the AAAI Conference on Artificial Intelligence, 21
Uncertainty in AI
Intrusion attempts due to self-propagating code are becoming an increasingly urgent problem, in part due to the homogeneous makeup of the internet. Recent advances in anomalybased intrusion detection systems (IDSs) have made use of the quickly spreading nature of these attacks to identify them with high sensitivity and at low false positive (FP) rates. However, slowly propagating attacks are much more difficult to detect because they are cloaked under the veil of normal network traffic, yet can be just as dangerous due to their exponential spread pattern. We extend the idea of using collaborative IDSs to corroborate the likelihood of attack by imbuing end hosts with probabilistic graphical models and using random messaging to gossip state among peer detectors. We show that such a system is able to boost a weak anomaly detector D to detect an order-of-magnitude slower worm, at false positive rates less than a few per week, than would be possible using D alone at the end-host or on a network aggregation point. We show that this general architecture is scalable in the sense that a fixed absolute false positive rate can be achieved as the network size grows, spreads communication bandwidth uniformly throughout the network, and makes use of the increased computation power of a distributed system. We argue that using probabilistic models provides more robust detections than previous collaborative counting schemes and allows the system to account for heterogeneous detectors in a principled fashion.