Detection of cyber-attacks and network attacks using Machine Learning
Department of Information Technology, JSPM’s Jayawantrao Sawant College of Engineering, Pune, Maharashtra, India.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(01), 128–132
Article DOI: 10.30574/wjaets.2024.12.1.0184
Publication history:
Received on 28 March 2024; revised on 17 May 2024; accepted on 20 May 2024
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.
Keywords:
Cyber-crime; Machine Learning algorithms; Phishing attack; Network Intrusion; Cross-Site Scripting (XSS); SQL Injection
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0