M.A. Thesis:

The combination of whale optimization algorithm,simulated annealing and Support Vector Machine for intrusion detection

In this paper, we introduce a intrusion detection framework by using a new method. We use a combination of whale optimization algorithm and simulated annealing algorithms to select the optimal properties of the properties mentioned in the data set, and then, with the help of the support vector machine classifier, the intrusion detection is done. The selection of useful features among several features in the dataset not only increases the functionality of the SVM, but also reduces the time and complexity of the computation.So this is an optimization problem that can be solved by exploratory algorithms In this paper, the proposed method is evaluated by performing tests with the NSL_KDD dataset. The empirical results show that the proposed method is better than the other methods in terms of detection rate and false alarm rate and other evaluation factors.The experimental results obtained from the present study, in the tables and diagrams, clearly confirm this .

Keywords: intrusion detection, whale algorithm, support vector machine, selection feature, simulated annealing