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Detecting Intrusions through System Call Sequence and Argument Analysis

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3 Author(s)
Federico Maggi ; Politecnico di Milano, Milano ; Matteo Matteucci ; Stefano Zanero

We describe an unsupervised host-based intrusion detection system based on system call arguments and sequences. We define a set of anomaly detection models for the individual parameters of the call. We then describe a clustering process that helps to better fit models to system call arguments and creates interrelations among different arguments of a system call. Finally, we add a behavioral Markov model in order to capture time correlations and abnormal behaviors. The whole system needs no prior knowledge input; it has a good signal-to-noise ratio, and it is also able to correctly contextualize alarms, giving the user more information to understand whether a true or false positive happened, and to detect global variations over the entire execution flow, as opposed to punctual ones over individual instances.

Published in:

IEEE Transactions on Dependable and Secure Computing  (Volume:7 ,  Issue: 4 )