Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems

被引:32
作者
Tabassum, Aliya [1 ]
Erbad, Aiman [2 ]
Mohamed, Amr [1 ]
Guizani, Mohsen [1 ]
机构
[1] Qatar Univ, Dept Comp Sci & Engn, Coll Engn, Doha, Qatar
[2] Hamad Bin Khalifa Univ, Qatar Fdn, Div Informat & Comp Technol, Coll Sci & Engn, Doha, Qatar
关键词
Deep learning; internet of things (IoT); intrusion detection system (IDS); incremental learning; pre-processing;
D O I
10.1109/ACCESS.2021.3051530
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.
引用
收藏
页码:14271 / 14283
页数:13
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