CCS2011: Enemy of the Good

Title

The Enemy of the Good: Re-evaluating Research Directions in Intrusion Detection

Abstract

Research in intrusion detection is in decline---there is less and less work being published in the field in competitive venues. Here we argue that a key reason for this decline is because of a misunderstanding of the significance and nature of false positive rates. False positives---legitimate behavior that is mis-classified as being potentially malicious---have a huge impact on the viability of any intrusion detection method in the real world. A survey of the literature, however, shows that false positive rates have remained persistently high in published reports. In this paper we argue that this persistence is due to the nature of the data sources used by intrusion detection systems. In support of this position, we present the requirements for viable intrusion detection systems, correlate those requirements with those of accurate detection methods, and then show that existing data sources cannot be so accurately modeled. To address these observations, we argue that research in intrusion detection must move away from the pure study of detection methods and towards the study of deployable detection/response mechanisms that directly accommodate relatively high false positive rates.

State of the Art in Machine Learning

Colin's section

Characteristics of IDS Data

Luc's section

The False Alarm Problem

(need better title)

Mohamed's section

Other Critiques of IDS

Discuss past work on criticizing IDS research

Discussion

synthetic versus real data issue attack distribution issue