C4.5: programs for machine learning
C4.5: programs for machine learning
ACM Transactions on Information and System Security (TISSEC)
Snort 2.0 Intrusion Detection
Intrusion detection techniques for mobile wireless networks
Wireless Networks
Bayesian Event Classification for Intrusion Detection
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
On the combination of naive Bayes and decision trees for intrusion detection
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
On the combination of naive Bayes and decision trees for intrusion detection
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
Parallelizing Feature Selection
Algorithmica
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
IEEE Transactions on Knowledge and Data Engineering
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
The New Front Line: Estonia under Cyberassault
IEEE Security and Privacy
Top 10 algorithms in data mining
Knowledge and Information Systems
High-order Markov kernels for intrusion detection
Neurocomputing
A Wrapper Method for Feature Selection in Multiple Classes Datasets
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A triangle area based nearest neighbors approach to intrusion detection
Pattern Recognition
Approximate Distributed K-Means Clustering over a Peer-to-Peer Network
IEEE Transactions on Knowledge and Data Engineering
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Intrusion Detection Systems and Intrusion Prevention Systems
Information Security Tech. Report
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Semi-supervised outlier detection based on fuzzy rough C-means clustering
Mathematics and Computers in Simulation
Communications of the ACM
Cyber-Threat Proliferation: Today's Truly Pervasive Global Epidemic
IEEE Security and Privacy
The use of artificial intelligence based techniques for intrusion detection: a review
Artificial Intelligence Review
A Survey of Outlier Detection Methods in Network Anomaly Identification
The Computer Journal
A rough set based decision tree algorithm and its application in intrusion detection
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Learning intrusion detection: supervised or unsupervised?
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Intrusion detection systems (IDS) are an important element in a network's defences to help protect against increasingly sophisticated cyber attacks. IDS that rely solely on a database of stored known attacks are no longer sufficient for effectively detecting modern day threats. This paper presents a novel anomaly detection technique that can be used to detect previously unknown attacks on a network by identifying attack features. This effects-based feature identification method uniquely combines k-means clustering, Naive Bayes feature selection and C4.5 decision tree classification for pinpointing cyber attacks with a high degree of accuracy in order to increase the situational awareness of cyber network operators.