Biblio

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

A major challenge for utilities is energy theft, wherein malicious actors steal energy for financial gain. One such form of theft in the smart grid is the fraudulent amplification of energy generation measurements from DERs, such as photo-voltaics. It is important to detect this form of malicious activity, but in a way that ensures the privacy of customers. Not considering privacy aspects could result in a backlash from customers and a heavily curtailed deployment of services, for example. In this short paper, we present a novel privacy-preserving approach to the detection of manipulated DER generation measurements.

In this paper, we focus on the security issues and challenges in smart grid. Smart grid security features must address not only the expected deliberate attacks, but also inadvertent compromises of the information infrastructure due to user errors, equipment failures, and natural disasters. An important component of smart grid is the advanced metering infrastructure which is critical to support two-way communication of real time information for better electricity generation, distribution and consumption. These reasons makes security a prominent factor of importance to AMI. In recent times, attacks on smart grid have been modelled using attack tree. Attack tree has been extensively used as an efficient and effective tool to model security threats and vulnerabilities in systems where the ultimate goal of an attacker can be divided into a set of multiple concrete or atomic sub-goals. The sub-goals are related to each other as either AND-siblings or OR-siblings, which essentially depicts whether some or all of the sub-goals must be attained for the attacker to reach the goal. On the other hand, as a security professional one needs to find out the most effective way to address the security issues in the system under consideration. It is imperative to assume that each attack prevention strategy incurs some cost and the utility company would always look to minimize the same. We present a cost-effective mechanism to identify minimum number of potential atomic attacks in an attack tree.