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contributor authorSrinath, Srikanth
contributor authorDey, Satadru
date accessioned2024-12-24T18:49:47Z
date available2024-12-24T18:49:47Z
date copyright6/29/2024 12:00:00 AM
date issued2024
identifier issn0022-0434
identifier otherds_146_06_064503.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4302826
description abstractModern battery systems exhibit a cyber-physical nature due to the extensive use of communication technologies in battery management. This makes modern cyber-physical battery systems vulnerable to cyber threats where an adversary can manipulate sensing and actuation signals to satisfy certain malicious objectives. In this work, we present a machine learning-based approach to enable resilience to adversarial attacks by detecting and estimating the attack and subsequently taking corrective action to mitigate the attack. In particular, we focus on false data injection type attacks on battery systems. The overall diagnostic algorithm consists of an adaptive boosting-based attack detector, a long short-term memory (LSTM) neural network-based attack estimator, and a corrective controller. The proposed algorithm is trained and tested by utilizing data from a complex battery electrochemical battery simulator. Simulation results are presented to verify the effectiveness of the approach.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Machine Learning Approach toward Cyber-Physical Security of Battery Systems
typeJournal Paper
journal volume146
journal issue6
journal titleJournal of Dynamic Systems, Measurement, and Control
identifier doi10.1115/1.4065703
journal fristpage64503-1
journal lastpage64503-9
page9
treeJournal of Dynamic Systems, Measurement, and Control:;2024:;volume( 146 ):;issue: 006
contenttypeFulltext


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