Abstract

Identifying new intrusions and developing effective signatures that detect them is essential for protecting computer networks. We present Nemean, a system for automatic generation of intrusion signatures from honeynet packet traces. Our architecture is distinguished by its emphasis on a modular design framework that encourages independent development and modification of system components and protocol semantics awareness which allows for construction of signatures that greatly reduce false alarms. The building blocks of our architecture include transport and service normalization, intrusion profile clustering and automata learning that generates connection and session aware signatures. We demonstrate the potential of Nemean’s semantics-aware, resilient signatures through a prototype implementation. We use two datasets to evaluate the system: (i) a production dataset for false-alarm evaluation and (ii) a honeynet dataset for measuring detection rates. Signatures generated by Nemean for NetBIOS exploits had a 0% false-positive rate and a 0.04 % false-negative rate. 1

Citations