Date of Award
Master of Science
Electrical and Computer Engineering
Advanced Metering Infrastructure (AMI) has been rapidly developed and widely used for the utility industry; meanwhile, it also has become an attractive target of different varieties of cyber-attacks due to AMI's security and privacy vulnerabilities as well as providing a way where one may steal energy. Therefore, it is crucial to develop a reliable, secure and efficient AMI network system with privacy protection. In this thesis, we introduce our data partitioning network system that splits the data into two separate partitions and transmits on one data channel with a privacy protection mechanism, an effective energy theft detection analyzer, a secure key exchange protocol, and a collaborative intrusion detection system in order to collect, transmit, manage, analyze and store energy information for the advanced metering infrastructure in smart grids. Security, privacy and energy theft are three main threats for AMI system. Our proposed method allows the server to check the integrity without decrypting the message by using homomorphic encryption techniques. Additionally, our anomaly-based energy theft detection method detects energy theft using fuzzy clustering techniques from data mining which has a minimum accuracy of 95\%. A collaborative intrusion detection system that distributes various detection techniques with different levels of computation complexity into different parts of the AMI network communication system is discussed. With the help of an encryption key exchange protocol and the collaborative intrusion detection system, it is shown that a potential access point denial-of-service attack triggered by a single smart meter can occur and a possible solution to mitigate the attack is provided. Simulation and analytical results show that our AMI network system design can provide secure, private and efficient communication with reasonable delay and overheads.
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