Intrusion Detection for Wireless Edge Networks Based on Federated Learning

被引:62
|
作者
Chen, Zhuo [1 ]
Lv, Na [1 ]
Liu, Pengfei [1 ]
Fang, Yu [1 ]
Chen, Kun [1 ]
Pan, Wu [1 ]
机构
[1] Air Force Engn Univ, Informat & Nav Coll, Xian 710077, Peoples R China
来源
IEEE ACCESS | 2020年 / 8卷 / 08期
基金
中国国家自然科学基金;
关键词
Intrusion detection; Data models; Collaborative work; Training; Image edge detection; Wireless communication; Servers; Wireless edge; intrusion detection; federated learning; gated recurrent unit;
D O I
10.1109/ACCESS.2020.3041793
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Edge computing provides off-load computing and application services close to end-users, greatly reducing cloud pressure and communication overhead. However, wireless edge networks still face the risk of network attacks. To ensure the security of wireless edge networks, we present Federated Learning-based Attention Gated Recurrent Unit (FedAGRU), an intrusion detection algorithm for wireless edge networks. FedAGRU differs from current centralized learning methods by updating universal learning models rather than directly sharing raw data among edge devices and a central server. We also apply the attention mechanism to increase the weight of important devices, by avoiding the upload of unimportant updates to the server, FedAGRU can greatly reduce communication overhead while ensuring learning convergence. Our experimental results show that, compared with other centralized learning algorithms, FedAGRU improves detection accuracy by approximately 8%. In addition, FedAGRU's communication cost is 70% less than other federated learning algorithms, and it exhibits strong robustness against poisoning attacks.
引用
收藏
页码:217463 / 217472
页数:10
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