A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network

• Main Authors: Ridwan M.A., Radzi N.A.M., Azmi K.H.M., Abdullah F., Ahmad W.S.H.M.W.
• Other Authors: 57193648099
• Format: Article
• Published: Science and Information Organization 2024
• Subjects: communication system; intrusion detection system; Machine learning; quality of service; routing algorithm; Classification (of information); Complex networks; Computer crime; Intrusion detection; Learning algorithms; MATLAB; Network security; Regression analysis; Routing algorithms; Communications systems; Hybrid intrusion detection; Intelligent routing algorithm; Intrusion Detection Systems; Machine-learning; Multi-protocol label-switching network; Performances evaluation; Quality-of-service; Routing information protocols; Service requirements; Quality of service

Machine Learning (ML) is seen as a promising application that offers autonomous learning and provides optimized solutions to complex problems. The current Multiprotocol Label Switching (MPLS)-based communication system is packed with exponentially increasing applications and different Quality-of-Services (QoS) requirements. As the network is getting complex and congested, it will become challenging to satisfy the QoS requirements in the MPLS network. This study proposes a hybrid ML-based intrusion detection system (ML-IDS) and ML-based intelligent routing algorithm (ML-RA) for MPLS network. The research is divided into three parts, which are (1) dataset development, (2) algorithm development, and (3) algorithm performance evaluation. The dataset development for both algorithms is carried out via simulations in Graphical Network Simulator 3 (GNS3). The datasets are then fed into MATLAB to train ML classifiers and regression models to classify the incoming traffic as normal or attack and predict traffic delays for all available routes, respectively. Only the normal traffic predicted by the ML-IDS algorithm will be allowed to enter the network domain, and the route with the fastest delay predicted by the ML-RA is assigned for routing. The ML-based routing algorithm is compared to the conventional routing algorithm, Routing Information Protocol version 2 (RIPv2). From the performance evaluations, the ML-RA shows 100 percent accuracy in predicting the fastest route in the network. During network congestion, the proposed ML outperforms the RIPv2 in terms of delay and throughput on average by 57.61 percent and 46.57 percent, respectively. � 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.