Artificial immune system based intrusion detection in a distributed hierarchical network architecture of smart grid

The advent of the smart grid promises to usher in an era that will bring intelligence, efficiency, and optimality to the power grid. Most of these changes will occur as an Internet-like communications network is superimposed on top of the current power grid using wireless technologies including 802.15.4, 802.11, and the Zigbee protocol. Each of these will expose the power grid to cyber security threats. In order to address this issue, this work proposes a Distributed Intrusion Detection System for Smart Grids (SGDIDS) by developing and deploying an intelligent module, the Analyzing Module (AM) in the multiple layers of smart grids. Multiple AMs will be embedded at each level of the smart grid - the home area network (HAN), neighborhood area network (NAN), and the Wide Area Network (WAN) - where they will use Artificial Immune System (AIS) to detect and classify malicious data and possible cyber attacks. AMs at each level will be trained using data that is relevant to their level and will also be able to communicate in order to improve detection. Simulation results demonstrate that this is a promising methodology for identifying malicious network traffic and improving system security.