archives

Learning Classifiers for Misuse and Anomaly Detection Using a Bag of System Calls Representation


Home 

About 

Browse 

Search 

Register 

Subscriptions 

Deposit Papers 

Help
    

Kang, Dae-Ki, Doug, Fuller and Vasant, Honavar (2005) Learning Classifiers for Misuse and Anomaly Detection Using a Bag of System Calls Representation. Technical Report, Computer Science, Iowa State University.

Full text available as:Adobe PDF

Abstract

In this paper, we propose a ``bag of system calls'' representation for intrusion detection in system call sequences and describe misuse and anomaly detection results with standard machine learning techniques on University of New Mexico (UNM) and MIT Lincoln Lab (MIT LL) system call sequences with the proposed representation. With the feature representation as input, we compare the performance of several machine learning techniques for misuse detection and show experimental results on anomaly detection. The results show that standard machine learning and clustering techniques on simple ``bag of system calls'' representation of system call sequences is effective and often performs better than those approaches that use foreign contiguous subsequences in detecting intrusive behaviors of compromised processes.

Subjects:Software: OPERATING SYSTEMS (C): Security and Protection (K.6.5)
Computing Methodologies: ARTIFICIAL INTELLIGENCE: Learning (K.3.2)
ID code:00000359
Deposited by:Dae-Ki Kang on 03 March 2005



Contact site administrator at: ssg@cs.iastate.edu