A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnec-tivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for mali-cious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection.

Automated summary

The growth in data generation and use of computer network devices has enhanced internet infrastructure, leading to complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems are crucial in monitoring network traffic for malicious activities. In this study, a fused machine learning technique is proposed for detecting intrusion in heterogeneous networks and protecting against malicious attacks, achieving a validation accuracy of 95.18% and a miss rate of 4.82% in intrusion detection.