Artificial immune system-based intrusion detection in a distributed hierarchical network architecture of smart grid
Dr. K. V. Rukmani, Lt. Dr. D. Antony Arul Raj, N.Mohammed Hisaan, Isaiyazhini, J.Harish, S.Sanjay
Bio inspired computing, Artificial Immune System (AIS), Wide Area Network (WAN), Analyzing Module (AM), Distributed Intrusion Detection System (DIDS), Malicious data detection.
The article titled "Artificial immune system based intrusion detection in a distributed hierarchical network architecture of smart grid" addresses the emerging challenges posed by cyber security threats to smart grids. With the integration of Internet-like communication networks into the traditional power grid infrastructure, smart grids become susceptible to various cyber attacks. To mitigate these risks, the authors propose a Distributed Intrusion Detection System for Smart Grids (SGDIDS) by deploying an intelligent module known as the Analyzing Module (AM) across different layers of the smart grid architecture. These layers include the home area network (HAN), neighbourhood area network (NAN), and the Wide Area Network (WAN). The AMs at each level utilize Artificial Immune System (AIS) techniques to detect and classify malicious data and potential cyber attacks. By training AMs with relevant data specific to their respective layers and enabling inter-level communication, the system enhances its capability to identify and respond to cyber threats effectively. Simulation results demonstrate the efficacy of this approach in identifying malicious network traffic, thereby bolstering the security of smart grid systems. The proposed SGDIDS presents a promising methodology for enhancing system security in smart grids, thus contributing to the realization of intelligent, efficient, and optimal power grid management. This work underscores the importance of proactive measures to safeguard critical infrastructure like smart grids against cyber threats, ensuring the reliability and resilience of the modern power grid ecosystem.
Article Details
Unique Paper ID: 163078

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 940 - 948
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