A Reinforcement Learning Approach for Host-Based Intrusion Detection Using Sequences of System Calls

@inproceedings{Xu2005ARL,
  title={A Reinforcement Learning Approach for Host-Based Intrusion Detection Using Sequences of System Calls},
  author={Xin Xu and Tao Xie},
  booktitle={ICIC},
  year={2005}
}
Intrusion detection has emerged as an important technique for network security. Due to the complex and dynamic properties of intrusion behaviors, machine learning and data mining methods have been widely employed to optimize the performance of intrusion detection systems (IDSs). However, the results of existing work still need to be improved both in accuracy and in computational efficiency. In this paper, a novel reinforcement learning approach is presented for host-based intrusion detection… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 11 references

Detecting Intrusions using System Calls: Alternative Data Models

IEEE Symposium on Security and Privacy • 1999
View 4 Excerpts
Highly Influenced

Learning to predict by the methods of temporal differences

Machine Learning • 1988
View 5 Excerpts
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Intrusion Detection Using Neural Networks and Support Vector Machines

S. Mukkamala, G. Janoski, A. H.Sung
Proceedings of IEEE International Joint Conference on Neural Networks • 2002
View 1 Excerpt