An Efficient Low Complexity Edge-Cloud Framework for Security in IoT Networks

被引:8
|
作者
Truong Thu Huong [1 ]
Ta Phuong Bac [1 ]
Dao Minh Long [1 ]
Bui Doan Thang [1 ]
Tran Duc Luong [1 ]
Nguyen Thanh Binh [2 ]
机构
[1] Hanoi Univ Sci & Technol, Sch Elect & Telecommun, Hanoi, Vietnam
[2] Hanoi Univ Sci & Technol, Sch Informat & Commun Technol, Hanoi, Vietnam
关键词
Internet of Things; Security; Multi-class detection; Feature Processing; INTERNET; THINGS;
D O I
10.1109/ICCE48956.2021.9352046
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Internet of Things (IoT) and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge-cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the cloud's workload. We also propose a multi-attack detection mechanism called LCHA (Low-Complexity detection solution with High Accuracy) , which has low complexity fir deployment at the edge zone while still maintaining high accuracy. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT-IoT data set. The results show that LCHA outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.
引用
收藏
页码:533 / 539
页数:7
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