Preventing Insider Threads in Networks Using Machine Learning

ManojKumar N, Akilandeswari A, KaviyaDharshini S

Department of Information Technology, Agni College of Technology, India

IJTCSE-ISSN 2349–1582

Volume No :10, Issue:02

Accepted for June 2023 Issue

ABSTRACT

The increasing complexity and sophistication of modern network attacks demand an effective and reliable intrusion detection system (IDS) to secure the network. In recent years, software-defined networking (SDN) has emerged as a promising solution to improve network security. In this paper, we propose an intrusion detection system using an software-defined networking algorithm. The proposed intrusion detection system architecture employs a centralized controller that is responsible for collecting and analyzing network traffic data from multiple switches in the network. The software-defined networking algorithm is used to detect anomalous traffic patterns and to trigger appropriate actions, such as isolating the affected host or blocking the malicious traffic. The performance evaluation of the proposed intrusion detection system using software-defined networking algorithm shows that it achieves high detection rates and low false positives. Moreover, the proposed intrusion detection system is scalable and flexible, making it suitable for large and complex networks. The results of our study demonstrate the potential of the proposed intrusion detection system using software-defined networking algorithm as a promising approach for network security.