Biblio

A popular countermeasure against IP piracy relies on obfuscating the Finite State Machine (FSM), which is assumed to be the heart of a digital system. In this paper, we propose to use a special class of non-group additive cellular automata (CA) called D1 * CA, and it's counterpart D1 * CAdual to obfuscate each state-transition of an FSM. The synthesized FSM exhibits correct state-transitions only for a correct key, which is a designer's secret. The proposed easily testable key-controlled FSM synthesis scheme can thwart reverse engineering attacks, thus offers IP protection.

The smart grid is an electrical grid that has a duplex communication. This communication is between the utility and the consumer. Digital system, automation system, computers and control are the various systems of Smart Grid. It finds applications in a wide variety of systems. Some of its applications have been designed to reduce the risk of power system blackout. Dynamic vulnerability assessment is done to identify, quantify, and prioritize the vulnerabilities in a system. This paper presents a novel approach for classifying the data into one of the two classes called vulnerable or non-vulnerable by carrying out Dynamic Vulnerability Assessment (DVA) based on some data mining techniques such as Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), and a machine learning tool such as Support Vector Machine Classifier (SVM-C) with learning algorithms that can analyze data. The developed methodology is tested in the IEEE 57 bus, where the cause of vulnerability is transient instability. The results show that data mining tools can effectively analyze the patterns of the electric signals, and SVM-C can use those patterns for analyzing the system data as vulnerable or non-vulnerable and determines System Vulnerability Status.