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


Ridwan, Mohammad Azmi and Mohamed Radzi, Nurul Asyikin and Mohd Azmi, Kaiyisah Hanis and Abdullah, Fairuz and Wan Ahmad, Wan Siti Halimatul Munirah (2023) A New Machine Learning-based Hybrid Intrusion Detection System and Intelligent Routing Algorithm for MPLS Network. International Journal of Advanced Computer Science and Applications, 14 (4). ISSN 2158-107X

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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.

Item Type: Article
Uncontrolled Keywords: Machine learning; intrusion detection system; routing algorithm; quality of service; communication system
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 02 Jun 2023 01:31
Last Modified: 02 Jun 2023 01:31
URII: http://shdl.mmu.edu.my/id/eprint/11451


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