Ouafae Elaeraj and Cherkaoui Leghris, Hassan II University of Casablanca, Morocco

With the increase in Internet and local area network usage, malicious attacks and intrusions into computer systems are growing. The design and implementation of intrusion detection systems became extremely important to help maintain good network security. Support vector machines (SVM), a classic pattern recognition tool, has been widely used in intrusion detection. They make it possible to process very large data with great efficiency and are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make the detection more effective.

Intrusion detection System, Support vector machines, Machine Learning.