Enhanced Cyber Attack Detection Process for Internet of Health Things (IoHT) Devices Using Deep Neural Network

被引:8
|
作者
Vijayakumar, Kedalu Poornachary [1 ]
Pradeep, Krishnadoss [1 ]
Balasundaram, Ananthakrishnan [2 ]
Prusty, Manas Ranjan [2 ]
机构
[1] Vellore Inst Technol, Sch Comp Sci & Engn, Chennai 600127, India
[2] Vellore Inst Technol, Ctr Cyber Phys Syst, Sch Comp Sci & Engn, Chennai 600127, India
关键词
cyber-attack; deep learning; IoHT; ANOMALY DETECTION; DETECTION SYSTEM; CLASSIFICATION; SECURITY;
D O I
10.3390/pr11041072
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Internet of Health Things plays a vital role in day-to-day life by providing electronic healthcare services and has the capacity to increase the quality of patient care. Internet of Health Things (IoHT) devices and applications have been growing rapidly in recent years, becoming extensively vulnerable to cyber-attacks since the devices are small and heterogeneous. In addition, it is doubly significant when IoHT involves devices used in healthcare domain. Consequently, it is essential to develop a resilient cyber-attack detection system in the Internet of Health Things environment for mitigating the security risks and preventing Internet of Health Things devices from becoming exposed to cyber-attacks. Artificial intelligence plays a primary role in anomaly detection. In this paper, a deep neural network-based cyber-attack detection system is built by employing artificial intelligence on latest ECU-IoHT dataset to uncover cyber-attacks in Internet of Health Things environment. The proposed deep neural network system achieves average higher performance accuracy of 99.85%, an average area under receiver operator characteristic curve 0.99 and the false positive rate is 0.01. It is evident from the experimental result that the proposed system attains higher detection rate than the existing methods.
引用
收藏
页数:14
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