Intrusion Detection Systems, Issues, Challenges, and Needs

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), support vector machine (SVM) have been used IDS in last decades. However, these classifiers is not working well if they applied alone without any other algorithms that can tune parameters or choose best sub set features problem. Such C SVM gamma which effect performance tuned well. Optimization genetic algorithm (GA), particle swarm optimization (PSO) , ant colony algorithm, along with to improve work detecting intrusion increase classifiers. suffer from lacks especially when apply detect new type attacks, need JAYA teaching learning-based (TLBO) arise. In this paper, we review IDS, state their strength weaknesses, provide researchers alternative could be use field future works.