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Real-time Cyber-Physical Security Solution Leveraging an Integrated Learning-Based Approach

Published: 09 January 2024 Publication History

Abstract

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks.

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    Published In

    cover image ACM Transactions on Sensor Networks
    ACM Transactions on Sensor Networks  Volume 20, Issue 2
    March 2024
    572 pages
    EISSN:1550-4867
    DOI:10.1145/3618080
    • Editor:
    • Wen Hu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

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    Publication History

    Published: 09 January 2024
    Online AM: 25 January 2023
    Accepted: 04 November 2022
    Revised: 21 September 2022
    Received: 18 May 2022
    Published in TOSN Volume 20, Issue 2

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    Author Tags

    1. Information security
    2. cyber energy
    3. integrated learning
    4. attack type identification

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    Funding Sources

    • National Science Foundation of China
    • Shanghai Sailing Program
    • National Science Foundation of Shanghai
    • Shanghai Chenguang Program

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    Cited By

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    • (2024)Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365647620:7(1-24)Online publication date: 15-May-2024
    • (2024)MultiRider: Enabling Multi-Tag Concurrent OFDM Backscatter by Taming In-band InterferenceProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661862(292-303)Online publication date: 3-Jun-2024
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    • (2024)CNN-GWO-voting & hybrid: ensemble learning inspired intrusion detection approaches for cyber-physical systemsProceedings of the Indian National Science Academy10.1007/s43538-024-00372-0Online publication date: 26-Nov-2024
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