TY - GEN
T1 - Intrusion Detection in IoT-Driven Cyber-Physical Systems
T2 - 11th International Conference on Computational Science and Computational Intelligence, CSCI 2024
AU - Khan, Muhammad Ali
AU - Rais, Rao Naveed Bin
AU - Khalid, Osman
AU - Bilal, Kashif
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Cyber-Physical Systems (CPS) integrate Internet of Things (IoT) technology to enable seamless communication and control across interconnected devices. However, IoT-driven CPS faces critical security challenges, including data breaches and denial-of-service attacks. Machine learning and deep learning techniques in Centralized Learning (CL) and Federated Learning (FL) settings are used for Intrusion Detection Systems (IDS), but the non-IID nature of IoT data complicates both approaches. Non-IID data, including data skew, affects model performance and convergence, with FL showing promise but requiring further exploration. This study investigates the performance of CL and FL in IDS for IoT-driven CPS under IID (balanced) and non-IID (imbalanced) scenarios. We compare both approaches using metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that FL outperforms CL in IID settings, achieving higher accuracy. Moreover, FL’s resilience is demonstrated under non-IID conditions, outperforming CL by up to 1.3% in certain cases. Given the decentralized and heterogeneous nature of IoT-driven CPS, FL proves to be a robust and scalable solution for intrusion detection.
AB - Cyber-Physical Systems (CPS) integrate Internet of Things (IoT) technology to enable seamless communication and control across interconnected devices. However, IoT-driven CPS faces critical security challenges, including data breaches and denial-of-service attacks. Machine learning and deep learning techniques in Centralized Learning (CL) and Federated Learning (FL) settings are used for Intrusion Detection Systems (IDS), but the non-IID nature of IoT data complicates both approaches. Non-IID data, including data skew, affects model performance and convergence, with FL showing promise but requiring further exploration. This study investigates the performance of CL and FL in IDS for IoT-driven CPS under IID (balanced) and non-IID (imbalanced) scenarios. We compare both approaches using metrics such as accuracy, precision, recall, and F1-score. Our findings reveal that FL outperforms CL in IID settings, achieving higher accuracy. Moreover, FL’s resilience is demonstrated under non-IID conditions, outperforming CL by up to 1.3% in certain cases. Given the decentralized and heterogeneous nature of IoT-driven CPS, FL proves to be a robust and scalable solution for intrusion detection.
KW - Internet of Things
KW - centralized learning
KW - cyber-physical systems
KW - federated learning
KW - intrusion detection
KW - non-iid
UR - https://www.scopus.com/pages/publications/105014161931
U2 - 10.1007/978-3-031-95133-6_5
DO - 10.1007/978-3-031-95133-6_5
M3 - Conference contribution
AN - SCOPUS:105014161931
SN - 9783031951329
T3 - Communications in Computer and Information Science
SP - 66
EP - 79
BT - Computational Science and Computational Intelligence - 11th International Conference, CSCI 2024, Proceedings
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Shenavarmasouleh, Farzan
A2 - Amirian, Soheyla
A2 - Ghareh Mohammadi, Farid
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 December 2024 through 13 December 2024
ER -