Ensemble Learning for Intrusion Detection in SDN-Based Zero Touch Smart Grid Systems

Abstract

Software-defined network (SDN) is widely deployed on Smart Grid (SG) systems. It consists in decoupling control and data planes, to automate the monitoring and management of the communication network, and thus enabling zero touch management of SG systems. However, SDN-based SG is prone to several security threats and varios type of new attacks. To alleviate these issues, various Machine/Deep learning (ML/DL)based intrusion detection systems (IDS) were designed to improve the detection accuracy of conventional IDS. However, they suffer from high variance and/or bias, which may lead to an inaccurate security threat detection. In this context, ensemble learning is an emerging ML technique that aims at combining several ML models; the objective is to generate less data-sensitive (i.e., less variance) and more flexible (i.e., less bias) machine learning models. In this paper, we design a novel framework, called BoostIDS, that leverages ensemble learning to efficiently detect and mitigate security threats in SDN-based SG system. BoostIDS comprises two main modules: (1) A data monitoring and feature selection module that makes use of an efficient Boosting Feature Selection Algorithm to select the best/relevant SG-based features; and (2) An ensemble learning-based threats detection moel that implements a Lightweight Boosting Algorithm (LBA) to timely and effectively detects SG-based attacks in a SDN environment. We conduct extensive experiments to validate BoostIDS on top of multiple real attacks; the obtained results using NSL-KDD and UNSW-NB15 datasets, confirm that BoostIDS can effectively detect/mitigate security threats in SDN-based SG systems, while optimizing training/test time complexity.

On Feature Selection Techniques for Detecting DoS Attacks with a Multi-class Classifier

“…El Houda et al [37] present the BoostIDS, a framework designed for the detection and mitigation of security threats in Smart Grid (SG) systems based on SND. The framework uses ensemble learning to address common challenges in intrusion detection systems using ML and Deep Learning (DL).…”

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

“…Likewise, a centralized grid topology is more prone to reliability issues, as a sudden fault involves a full blackout. Introducing new information and communication technologies is a good solution to deal with energy issues [161]. Smart Grid (SG) is an intelligent and distributed digital power system designed to effectively utilize the electricity network.…”

Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey

“…Flow Filtration [103][104][105][106][107][108] Machine Learning [118][119][120] Figure Akkaya et al [8] investigated the use of software-defined networking (SDN) in wireless local networks (WLANs) for SG applications. The authors begin by outlining the difficulties that traditional WLANs face when supporting SG applications, such as scalability, reliability, and security.…”

“…According to the analysis conducted in [118], cyber-attack detection solutions based on a single machine model encounter issues like poor generalization and ineffective detection of all attack types. Zakaria et al [119] design BoostIDS which is a novel framework that leverages ensemble learning to efficiently detect and mitigate security threats like DDoS, probe, fuzzers, and backdoor attacks in SD-SG. BootIDS is deployed as an application in the application plane of the SDN architecture and consists of two modules.…”