AI Driven Approach for Detecting False Data Injection Attacks Targeting Water-Energy Nexus

Ahmed Abughali, Mohamad Alansari, Ameena S. Al-Sumaiti

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations

    Abstract

    As water and energy grids are highly interconnected, water-energy nexus (WEN) is considered an attractive target for malicious attackers. Attacking WEN affects both systems simultaneously; consequently, the attack is able to create fatal damage in the entire network. In this paper, we propose to tackle this problem using different deep learning models with different loss functions. Additionally, a mixed-integer nonlinear programming (MINLP) WEN in presence of renewable energy sources model is reformulated into a mixed-integer linear programming model for real-time implementation purposes. The optimization model is developed using MATALB, and the deep-learning models are implemented using Python keras framework.

    Original languageBritish English
    Title of host publication2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies, GlobConHT 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9798350332117
    DOIs
    StatePublished - 2023
    Event2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies, GlobConHT 2023 - Male, Maldives
    Duration: 11 Mar 202312 Mar 2023

    Publication series

    Name2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies, GlobConHT 2023

    Conference

    Conference2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies, GlobConHT 2023
    Country/TerritoryMaldives
    CityMale
    Period11/03/2312/03/23

    Keywords

    • cyber attacks
    • deep learning
    • long short-term memory (LSTM)
    • optimization
    • transformer
    • Water-energy nexus (WEN)

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