联合学习
计算机科学
入侵检测系统
人工智能
机器学习
骨料(复合)
互联网
权力下放
深度学习
数据共享
信息隐私
计算机安全
万维网
医学
材料科学
替代医学
政治学
法学
病理
复合材料
作者
Ammar Alazab,Ansam Khraisat,Sarabjot Singh,Tony Jan,Mamoun Alazab
出处
期刊:Electronics
[MDPI AG]
日期:2023-08-08
卷期号:12 (16): 3382-3382
被引量:1
标识
DOI:10.3390/electronics12163382
摘要
Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.
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