Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses

Abstract

IntroductionThe development of the Internet has made life much more convenient, but forms of network intrusion have become increasingly diversified and the threats to network security are becoming much more serious. Therefore, research into intrusion detection has become very important for network security.MethodsIn this paper, a clustering algorithm based on the symbiotic-organism search (SOS) algorithm and a Kohonen neural network is proposed.ResultsThe clustering accuracy of the Kohonen neural network is improved by using the SOS algorithm to optimize the weights in the Kohonen neural network.DiscussionOur approach was verified with the KDDCUP99 network intrusion data. The experimental results show that SOS-Kohonen can effectively detect intrusion. The detection rate was higher, and the false alarm rate was lower.

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model

“…There are a few intelligent computation algorithms. These include a chimp-inspired optimization scheme [ 39 ], i.e., an intelligent optimization algorithm effectively exploited to solve different problems with reasonably accuracy through providing a good balance in the exploration and exploitation phases; a Kohonen neural network [ 40 ], i.e., an unsupervised self-organizing (SO) competitive neural network that performs automatic clustering and that updates the weights of the network through SO feature mapping with effective application to intrusion detection of the network virus; and a Mayfly algorithm [ 41 ], i.e., a swarm intelligence-based heuristic approach, applied to successfully solve different engineering optimization problems, including the asymmetric traveling salesman problem, due to the features of population diversity and enhanced local search capability. Others include a simplified slime mould algorithm [ 42 ], i.e., a modified version of the slime mould heuristic, with an introduction of enhanced adaptive oscillation for better exploration capability during the early search phase, with application to wireless sensor network optimization problems; a code pathfinder algorithm [ 43 ], i.e., a discrete complex code pathfinder heuristic for an efficient solution to the optimization problem of wind farm layout through an improved exploration capability; and a firefighting strategy based marine predators approach [ 44 ], i.e., an improved variant of marine predator heuristic through an introduction of opposition-based learning for more uniform initial population and adaptive weight factor for creating balance between exploration/exploitation capabilities to effectively handle the forest fire rescue issues.…”