Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Efficient mining of association rules using closed itemset lattices
Information Systems
Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
Intrusion detection using autonomous agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Automated discovery of concise predictive rules for intrusion detection
Journal of Systems and Software
A data mining framework for constructing features and models for intrusion detection systems (computer security, network security)
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
Agent-Based Network Intrusion Detection System Using Data Mining Approaches
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Modeling Intrusion Detection System by Discovering Association Rule in Rough Set Theory Framework
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
CAMNEP: agent-based network intrusion detection system
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Network Intrusion Detection System Using Neural Networks
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
A New Data-Mining Based Approach for Network Intrusion Detection
CNSR '09 Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference
Intrusion Detection Method Using Neural Networks Based on the Reduction of Characteristics
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A new generic basis of "factual" and "implicative" association rules
Intelligent Data Analysis
A Self-Organized Multiagent System for Intrusion Detection
Agents and Data Mining Interaction
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Expert Systems with Applications: An International Journal
A Snort-based agent for a JADE multi-agent intrusion detection system
International Journal of Intelligent Information and Database Systems
G-means: a clustering algorithm for intrusion detection
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as Intrusion Detection System (IDS). The IDS need to be accurate, adaptive, and extensible. Although many established techniques and commercial products exist, their effectiveness leaves room for improvement. A great deal of research has been carried out on intrusion detection in a distributed environment to palliate the drawbacks of centralized approaches. However, distributed IDS suffer from a number of drawbacks e.g. , high rates of false positives, low efficiency, etc. In this paper, we propose a distributed IDS that integrates the desirable features provided by the multi-agent methodology with the high accuracy of data mining techniques. The proposed system relies on a set of intelligent agents that collect and analyze the network connections, and data mining techniques are shown to be useful to detect the intrusions. Carried out experiments showed superior performance of our distributed IDS compared to the centralized one.