Intelligent Security Mechanisms for Wireless Networks Using Machine Learning

Abstract

Wireless networks are important to the modern communication systems but face severe threats of cyber-attacks. Though traditional security measures, such as encryption, firewall, and intrusion detection system, are effective up to a limit, they still have major limitations, such as manual updating on a regular basis, reactive threat detection, and scalability issues. This paper investigates the application of machine learning techniques to enhance wireless network security. ML, therefore, enables the power to learn by itself through data and adjust according to new threats for detecting any anomaly in network traffic preemptively. This paper thus evaluates the relative effectiveness of using different ML algorithms like Support Vector Machines, Random Forest, and Neural Networks for wireless network protection. The experiment shows that such models can further improve accuracy of threat detection and provide scalable and adaptable solutions to problems related to wireless network security. This has provided great findings in efforts to embed ML into security frameworks of great help in providing solid protection against advanced cyber-attacks, thus making wireless networks more secure and persistent.

Key words: Wireless networks, Cyber-attacks, Network security, Machine learning (ML), Anomaly detection, Support Vector Machines (SVM), Random Forest, Neural Networks, Network traffic analysis.