On the limits of payload-oblivious network attack detection

We introduce a methodology for evaluating network intrusion detection systems using an observable attack space, which is a parameterized representation of a type of attack that can be observed in a particular type of log data. Using the observable attack space for log data that does not include payload (e.g., NetFlow data), we evaluate the effectiveness of five proposed detectors for bot harvesting and scanning attacks, in terms of their ability (even when used in conjunction) to deter the attacker from reaching his goals. We demonstrate the ranges of attack parameter values that would avoid detection, or rather that would require an inordinately high number of false alarms in order to detect them consistently. © 2008 Springer-Verlag Berlin Heidelberg.

Duke Scholars

Author Michael Reiter Computer Science