Network intrusion detection through artificial immune system

Intrusion Detection Systems (IDS) are security technologies. In this regard, Artificial Immune System (AIS) which provides distributed detection through its lymphocytes is an appealing approach for designing IDSs. In this paper, an AIS based intrusion detection is proposed in which two sets of antib...

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Bibliographic Details
Main Authors: Tabatabaefar, Marzie, Miriestahbanati, Maryam, Gregoire, Jean-Charles
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Anomaly Detection
Artificial Immune System
Artificial intelligence
Data classifier
Detector Generation' Machine learning
Detectors
Heuristic algorithms
Immune system
Intrusion detection
Network Security
Particle swarm optimization
Training
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Summary:Intrusion Detection Systems (IDS) are security technologies. In this regard, Artificial Immune System (AIS) which provides distributed detection through its lymphocytes is an appealing approach for designing IDSs. In this paper, an AIS based intrusion detection is proposed in which two sets of antibodies - positive and negative - are generated for normal and attack samples respectively using negative selection and positive selection theories in primary detectors' generation. Standard Particle Swarm Optimization (PSO) is employed for training immature detectors to improve detection rate. Moreover, antibodies' radiuses is dynamically determined through generation and training algorithms. Simulation shows that the proposed algorithm achieved 99.1% true positive rate while the false positive rate is 1.9%.
ISSN:2472-9647
DOI:10.1109/SYSCON.2017.7934751