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Intelligent Intrusion Detection in Computer Networks using Swarm Intelligence
Author(s) -
Apoorv Saxena,
Carsten Mueller
Publication year - 2018
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2018916224
Subject(s) - computer science , ant colony optimization algorithms , intrusion detection system , swarm intelligence , ant colony , artificial intelligence , swarm behaviour , simplicity , collective intelligence , intrusion , machine learning , data mining , particle swarm optimization , philosophy , epistemology , geochemistry , geology
Swarm Intelligence is inspired by the collective behaviour of many individuals. It is coordinated using decentralized control and self-organization. The individual simplicity and their complex group behaviours can outperform the vast majority of individual members when solving problems and making decisions. During recent years, the number of attacks on networks has dramatically increased and consequently, interest in network intrusion detection has increased among the researchers. In this research paper, a software architecture is modelled and implemented which uses Ant Colony Optimization (ACO), ACO is combined with Non-Negative Matrix Factorization method for classifying a computer network behaviour as a sequence of system calls.

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