Rapid APT Detection in Resource-Constrained IoT Devices Using Global Vision Federated Learning (GV-FL)

Han Zhu, Huibin Wang, Chan Tong Lam, Liyazhou Hu, Benjamin K. Ng, Kai Fang

研究成果: Conference contribution同行評審

摘要

Security and privacy are critical concerns in cyberspace due to the inherent vulnerability of Internet of Things (IoT) systems. In particular, Advanced Persistent Threat (APT) has become one of the most severe security threats in cyberspace. Therefore, how to breach the limitation of traditional network security detection techniques focusing on specific attack patterns has attracted widespread attention. To cope with APT attacks, this article proposes a new approach, Global Vision Federated Learning (GV-FL), which utilizes FL for accurate and efficient APT detection in resource-constrained IoT devices. Specifically, the proposed method implements the identification of APT attacks based on the FL framework, which leverages FL for distributed, privacy-preserving learning of the network. Considering the advanced and persistent nature of APT, the local model of each IoT device is aggregated into a global model for fast detection of APT in resource-limited devices. In addition, the proposed GV-FL approach is comprehensively compared with existing detection methods. Experimental results show that the GV-FL approach not only outperforms existing detection methods in terms of detection accuracy and speed but also significantly reduces resource consumption, thus the GV-FL approach is a promising APT detection approach in the IoT domain.

原文English
主出版物標題Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings
編輯Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
發行者Springer Science and Business Media Deutschland GmbH
頁面568-581
頁數14
ISBN(列印)9789819981250
DOIs
出版狀態Published - 2024
事件30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China
持續時間: 20 11月 202323 11月 2023

出版系列

名字Communications in Computer and Information Science
1961 CCIS
ISSN(列印)1865-0929
ISSN(電子)1865-0937

Conference

Conference30th International Conference on Neural Information Processing, ICONIP 2023
國家/地區China
城市Changsha
期間20/11/2323/11/23

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