Open AccessProgress of Machine Learning in the Field of Intrusion Detection Systems
Author(s) -
Ouafae Elaeraj,
Cherkaoui Leghris
Publication year - 2021
Publication title -
international journal of cryptography and information security
Language(s) - English
Resource type - Journals
ISSN - 1839-8626
DOI - 10.5121/ijcis.2021.11401
Subject(s) - intrusion detection system , computer science , support vector machine , constant false alarm rate , anomaly based intrusion detection system , machine learning , data mining , artificial intelligence , field (mathematics) , gaussian function , network security , false alarm , pattern recognition (psychology) , gaussian , computer security , physics , mathematics , quantum mechanics , pure mathematics
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, 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 detection more efficient.