USING MACHINE LEARNING FOR INTRUSION DETECTION SYSTEMS

Given the importance of the computer systems in our daily life today, it is decisive to be able to protect the computer systems against attacks. Intrusion De-tection Systems (IDSs) are the crucial component of modern cybersecurity systems. IDSs are built-in in the devices of the major providers such as Cisco and Juniper. Since the early days of the Internet up to now, the IDSs rely heavily on signature-based detection methods. However, in recent years, researchers utilize the power of machine learning techniques and achieve very good performance in classifying network attacks. In this paper, we analyze the machine learning techniques that have been proposed in recent years. We propose some new techniques to improve the performance of the existing methods. The experimental results using real-world datasets show that our suggestions can boost the predictive accuracy of the models.