Detection And Mitigation Of Distributed Denial Of Service (Ddos) Attack: Application To Smart Grid Communication Networks

Abstract

The Smart Grid is an improvement on the conventional grid that uses advanced communication methods and new technology for the production, transmission, and distribution of electrical power. The modern Smart Grid \u27s ability to function successfully depends heavily on its communication infrastructure. Today, the usage of communication technology promotes energy efficiency, coordination amongst all Smart Grid components, from generation to end users, and optimal Smart Grid functioning. The communication network of the Smart Grid exchanges data regarding the condition of its numerous integrated IEDs (intelligent electronic devices); however, there are always chances for attackers to interrupt utility resources, interfere with communication networks, or steal customers\u27 intellectual property and private information due to the different amounts of IEDs connected across Smart Grid Communication Networks. Additionally, as Distributed Energy Resources (DER) and dynamic loads become more prevalent, phase angle values that are crucial for Phasor Measurement Units (PMUs) change, and real-time control has emerged as a key tool for tracking power system performance in today\u27s Smart Grid technology. Because of their link to the Smart Grid \u27s communication network, Phasor Measurement Units devices are now susceptible to cyberattacks. Because of the recent global security incidents and new cyberthreats, this development has created new cyber-security issues for the Smart Grid and is a very worrying issue. The effects of Distributed Denial of Service (DDOS) assaults on PMU data transfers over Smart Grid communication networks in the form of NetFlows were carefully examined in this study. For the first time in the literature, a combination of the Secure Network Analytics (SNA) tool, Intrusion Detection System, and firewall were used to model the DDOS attack in the Smart Grid \u27s communication network. Additionally, risk reduction and good security hygiene are enhanced by employing the Secure Network Analytics (SNA) tool to establish a security baseline for the Smart Grid system. The research findings are in contrast with those found in previous studies. Our findings demonstrated that this research strategy outperformed previous approaches in the literature in terms of mitigating and detecting DDOS attacks. Index Terms: Detection and mitigation, distributed denial of service (DDOS) attack, distributed energy resources, firewall, intrusion detection and prevention systems, phase measurement units, Smart Grid system