지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.6
- 수록면
- 648 - 668 (21page)
- DOI
- 10.7840/kics.2023.48.6.648
이용수
초록· 키워드
오류제보하기
In federated learning, the synchronous approach employed by the aggregating algorithm in the federal server, such as federated averaging (FedAVG), introduces high network communication costs, thus rendering it unsuitable for securing a network of unmanned aerial vehicles. This approach impedes the convergence speed of the global model and degrades its performance by increasing the number of participants. This study proposes a novel optimized aggregating algorithm called delay-aware truncated accuracy-(DATA)-based FedAVG. DATA-FedAVG is robust to the contingencies of straggling edge servers/clients (owing to network connectivity issues and system heterogeneity) and adaptively selects the fraction of clients whose model parameters are to be utilized in building the global model, thus optimally detecting intrusions in the network. In addition, the truncated client selection mechanism applied by DATA-FedAVG allows only clients with high-accuracy contributions to participate in both local training and federal updates. Extensive simulation experiments performed with a cybersecurity dataset validate the high performance of the proposed model and its reliability in accurately detecting attacks within an almost 75% reduced communication cost, while improving the performance of the intrusion detection model in terms of the average accuracy, recall, precision, and F1-score by 2%, 3%, 3%, and 3%, respectively.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Background of Study and Related Works
Ⅲ. System Model and Implementation
Ⅳ. Result Discussion and Performance Evaluation
Ⅴ. Conclusion
References
참고문헌 (32)
참고문헌 신청
B. Brik, A. Ksentini, and M. Bouaziz, “Federated learning for uavs-enabled wireless networks: Use cases, challenges, and open problems,” IEEE Access, vol. 8, pp. 53841-53849, 2020. (https://doi.org/10.1109/ACCESS.2020.2981430)
Q. Jiang, et al., “Intelligent intrusion detection based on federated learning for edge-assisted internet of things,” Secur. and Commun. Netw., 2021. [Online] Available: https://arxiv.org/abs/2106.09527.
V. U. Ihekoronye, S. O. Ajakwe, D.-S. Kim, and J. M. Lee, “Hierarchical intrusion detection system for secured military drone network: A perspicacious approach,” in MILCOM 2022-2022 IEEE, pp. 336-341, 2022. (https://doi.org/10.1109/MILCOM55135.2022.100175
B. Fraser, S. Al-Rubaye, S. Aslam, and A. Tsourdos, “Enhancing the security of unmanned aerial systems using digital-twin technology and intrusion detection,” in 2021 IEEE/AIAA 40th DASC, pp. 1-10, 2021. (https://doi.org/10.1109/DASC52595.2021.9594321)
N. I. Mowla, N. H. Tran, I. Doh, and K. Chae, “Federated learning-based cognitive detection of jamming attack in flying adhoc network,” IEEE Access, vol. 8, pp. 4338-4350, 2020. [Online] Available: https://ieeexplore.ieee.org/document/8945183.
Q. Jiang, et al., “Intelligent intrusion detection based on federated learning for edge-assisted internet of things,” Secur. and Commun. Netw., 2021. [Online] Available: https://arxiv.org/abs/2106.09527.
V. U. Ihekoronye, S. O. Ajakwe, D.-S. Kim, and J. M. Lee, “Hierarchical intrusion detection system for secured military drone network: A perspicacious approach,” in MILCOM 2022-2022 IEEE, pp. 336-341, 2022. (https://doi.org/10.1109/MILCOM55135.2022.100175
B. Fraser, S. Al-Rubaye, S. Aslam, and A. Tsourdos, “Enhancing the security of unmanned aerial systems using digital-twin technology and intrusion detection,” in 2021 IEEE/AIAA 40th DASC, pp. 1-10, 2021. (https://doi.org/10.1109/DASC52595.2021.9594321)
N. I. Mowla, N. H. Tran, I. Doh, and K. Chae, “Federated learning-based cognitive detection of jamming attack in flying adhoc network,” IEEE Access, vol. 8, pp. 4338-4350, 2020. [Online] Available: https://ieeexplore.ieee.org/document/8945183.
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