Machine learning in SDN networks for secure industrial cyber physical systems: a case of detecting link flooding attack. (5th May 2022)

• Record Type: Journal Article
• Authors: Deliwala, Priyanshi; Jhaveri, Rutvij H.; Ramani, Sagar
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 1(2022)
• Page Start: 76
• Page End: 84
• Publication Date: 2022-05-05
• Subjects: software-defined networking -- SDN -- random forest -- OpenFlow -- machine learning -- link flooding attacks -- LFA -- industrial cyber physical systems -- ICPSs; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Software-defined networking (SDN) is an emerging network architecture which has potential to solve real-time challenges of industrial cyber physical systems (ICPSs). However, it also opens a gateway to different network attacks such as link flooding attack (LFA). This paper illustrates how SDN's control layer is endangered to LFA. While the proposed approach (Iwendi et al., 2020a) can appear suitable on the surface, some weaknesses and anomalies are discovered when deliberated deeper. In the current paper, we point out these anomalies and the limitations of those anomalies by applying two machine learning algorithms, namely ANN-MLP and random forest to correctly classify the virulent traffic during congestion. We carry out experiments on Mininet network emulator and use the WEKA tool to assess the metrics by utilising the available datasets. The results show that a random forest can accurately detect virulent traffic and efficiently terminate it under congestion.