Open AccessPerformances of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) Using KDD Cup ‘99 Dataset in Intrusion Detection System (IDS)
Author(s) -
S. Norwahidayah,
Noraniah,
N. Farahah,
Ainal Amirah,
Nur Izzati Liyana,
N. Suhana
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1874/1/012061
Subject(s) - particle swarm optimization , artificial neural network , intrusion detection system , computer science , artificial intelligence , benchmark (surveying) , matlab , genetic algorithm , data mining , machine learning , geodesy , geography , operating system
Nowadays, the number of attacker increasing fast due to the current technologies. Most companies or an organization use Intrusion Detection System (IDS) to protect their network system. Many researchers suggest different ways to improve the IDS such using optimization techniques. Artificial Intelligence (AI) methods also proposed in IDS to attained high accuracy of detection for example; artificial neural network, particle swarm optimization and genetic algorithm. Artificial neural network (ANN) and Particle Swarm Optimization (PSO) used in this paper to equate the method and performances in IDS environment. The ANN output value will be compared with the result where ANN supported by PSO to produce higher accurate value. KDD CUP ’99 Dataset used as the benchmark of IDS and will be simulated in MATLAB Simulink 2013. 200 datasets used consists of attacks and normal activities as an input. In this paper, DoS attack which are Smurf and Neptune attacks selected for detection.