Combating Intrusions in Smart Grid: Practical Defense and Forecasting Approaches

Abstract

The smart grid represents one the biggest growth potentials of the Internet of Things (IoT) use case. The smart grid communication is a typical example of the inter-machine communication, which is popularly referred to as the Machine to Machine (M2M) communications whereby the deployed “things” such as smart meters and numerous sensors require none/minimal human intervention to characterize power requirements and energy distribution. The plethora of sensors have the ability to report back critical information like power consumption of the users and other monitoring signals on power quality to the control center. Thus, the energy distribution grid is coupled with the IoT sensing and delivery networks in the smart grid. However, this inherent design of the smart grid poses a significant security challenge, particularly from the networking domain, in terms of malicious events like Distributed Denial of Service (DDoS) attacks against smart meters and other devices. In this chapter, we overview two attack scenarios in the smart grid, at the Home Area Network (HAN) and the Building Area Network (BAN), respectively. HAN is a key part of the smart grid communications framework through which the customers are able to communicate with the electricity provider. In a HAN, there is typically a smart-meter and a number of electric appliances which communicate over ZigBee (IEEE 802.15.4) wireless technology. Even though ZigBee incorporates some security features, the technology still suffers from a number of security vulnerabilities in the smart grid environment. To demonstrate this, we present a HANIdentifier (HANId) conflict attack against ZigBee for HAN communications and demonstrate the impact of the attack on the smart grid communications. Then, an appropriate framework is presented to prevent the attack from taking place. Next in the chapter, we introduce more advanced concept using Gaussian Process to model the malicious attacks on a broader network level which may compromise the security and privacy of smart grid users. Based on our Gaussian Process based model, a lightweight and practical method to forecast intrusions in the smart grid communication network is proposed. By leveraging the proposed approach, the smart grid control center is able to predict malicious attacks so that early action can be taken to protect the smart grid from being adversely affected. Simulations results demonstrate the viability of the proposed forecasting method.