IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Communications of the ACM
Intrusion detection
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Anomaly Detection over Noisy Data using Learned Probability Distributions
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
An Application of Machine Learning to Network Intrusion Detection
ACSAC '99 Proceedings of the 15th Annual Computer Security Applications Conference
A data mining framework for constructing features and models for intrusion detection systems (computer security, network security)
Monitoring SIP Traffic Using Support Vector Machines
RAID '08 Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
PE-Miner: Mining Structural Information to Detect Malicious Executables in Realtime
RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
Behavioral distance measurement using hidden markov models
RAID'06 Proceedings of the 9th international conference on Recent Advances in Intrusion Detection
Modeling user search behavior for masquerade detection
RAID'11 Proceedings of the 14th international conference on Recent Advances in Intrusion Detection
Incorporating soft computing techniques into a probabilistic intrusion detection system
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Intrusion detection through learning behavior model
Computer Communications
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Intrusion detection systems are based on two fundamental approaches: the detection of anomalous behavior as it deviates from normal behavior, and misuse detection by monitoring those "signatures" of those known malicious attacks and system vulnerabilities. Anomaly (behavior-based) IDSs assume the deviation of normal activities under attacks and perform abnormal detection compared with predefined system or user behavior reference model. This paper is to provide a survey of anomaly intrusion detection techniques. It presents a review about the evolution of intrusion detection systems over the past two decades. It focuses on recent research advances and trends in anomaly IDSs, including the application of statistics, machine learning, neural network, computer immunology, and data mining techniques.