Detection of cyber-attacks and network attacks using Machine Learning

Abstract

The Internet and computer networks have become an important part of organizations and everyday life. New threats
and challenges have emerged to wireless communication systems especially in cyber security and network attacks. The
network traffic must be monitored and analysed to detect malicious activities and attacks. Recently, machine learning
techniques have been applied toward the detection of network attacks. In cyber security, machine learning approaches
have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam
detection, and phishing detection. As a result, effective adaptive methods, such as machine learning techniques, can
yield higher detection rates, lower false alarm rates and cheaper computing and transmission costs. Our key goal is
detection of cyber security and network attacks such as IDS, phishing and XSS, SQL injection, respectively. The proposed
strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the
system of the attack's existence in the early stages of the attack.