Jayesh Soni
ABSTRACT
Host based intrusion detection systems monitor operations for significant deviations from normal and healthy behavior. Anomalies are patterns in data that do not conform to the expected normal behavior. System call analysis has been conclusively established as the best method to reveal details about the program behavior. Therefore, attackers create new exploits that makes major impact at the system call level. In this research, we developed an enhanced and optimized deep learning LSTM (Long Short Term Memory) network, for anomaly detection, trained on sequences of system calls. Our model detects any anomalous behavior in the system calls with 80% accuracy.
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Digital Library
- Bayer, Justin, Osendorfer, Christian, Chen, Nutan, Urban, Sebastian, and van der Smagt, Patrick. On fast dropout and its applicability to recurrent networks. arXiv preprint arXiv:1311.0701, 2013.Google Scholar
- Sutskever, Ilya, Vinyals, Oriol, and Le, Quoc VV. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pp. 3104--3112, 2014. Google ScholarDigital Library
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