Intrusion Detection Systems, Issues, Challenges, and Needs - Aljanabi, Mohammad; Mohd Arfian, Ismail; Ali, Ahmed Hussein (Atlantis Press B.V., 2021)
Intrusion detection systems (IDSs) are one of the promising tools for protecting data and networks; many classification algorithms, such as neural network (NN), Naive Bayes (NB), decision tree (DT), and support vector machine (SVM) have been used for IDS in the last decades. However, these classifiers is not working well if they applied alone without any other algorithms that can tune the parameters of these classifiers or choose the best sub set features of the problem. Such parameters are C in SVM and gamma which effect the performance of SVM if not tuned well. Optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm , ant colony algorithm, and many other algorithms are used along with classifiers to improve the work of these classifiers in detecting intrusion and to increase the performance of these classifiers. However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. In this paper, we review the classifiers and optimization algorithms used in IDS, state their strength and weaknesses, and provide the researchers with alternative algorithms that could be use in the field of IDS in future works.