ISSN (Online) : 2456 - 0774

Email : ijasret@gmail.com

ISSN (Online) 2456 - 0774

A SURVEY ON MACHINE LEARNING BASED CYBER-ATTACK DETECTION TECHNIQUES FOR DISTRIBUTION SYSTEMS

Abstract

Abstract:- Critical infrastructure systems are of majorimportance to society as they have a great impact on people’s lives and the economy.Examples include the energy systems, telecommunication systems and watersupply. These cyber-physical systems are operated by means of computers andapplications using two-way communication capabilities and distributedintelligence to enhance efficiency, reliability, and stability. Attacks oncyber-physical systems have recently increased in frequency, impact andpublicity. Cyber-physical false data attack detection methods are presented inthis research paper which can protect the operation of power transmission anddistribution systems. This is achieved by automatically inferring underlyingphysical relationships using cross-sensor analytics in order to detect sensorfailures, replay attacks and other data integrity issues in real-time.

Keyword- CyberattackDetection, Machine Learning, Bayes Classification, LSTM

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