SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization

被引:33
作者
Faruqui, Nuruzzaman [1 ,2 ]
Abu Yousuf, Mohammad [2 ]
Whaiduzzaman, Md [3 ]
Azad, A. K. M. [4 ]
Alyami, Salem A. [5 ]
Lio, Pietro [6 ]
Kabir, Muhammad Ashad [7 ]
Moni, Mohammad Ali [8 ,9 ]
机构
[1] Daffodil Int Univ, Dept Software Engn, Dhaka 1216, Bangladesh
[2] Jahangirnagar Univ, Inst Informat Technol, Dhaka 1342, Bangladesh
[3] Queensland Univ Technol, Fac Sci, Sch Informat Syst, 2 George St, Brisbane, QLD 4000, Australia
[4] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Fac Sci, Dept Math & Stat, Riyadh 13318, Saudi Arabia
[5] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Coll Sci, Dept Math & Stat, Riyadh 11564, Saudi Arabia
[6] Univ Cambridge, Dept Comp Sci & Technol, Cambridge CB2 1TN, England
[7] Charles Sturt Univ, Sch Comp Math & Engn, Bathurst, NSW 2795, Australia
[8] Univ Queensland, Fac Hlth & Behav Sci, Sch Hlth & Rehabil Sci, Artificial Intelligence & Data Sci, Brisbane, Qld 4072, Australia
[9] Charles Stuart Univ, Artificial Intelligence & Cyber Futures Inst, Bathurst, NSW 2795, Australia
关键词
internet of medical things; intrusion detection system; convolutional neural network; long short-term memory; response mechanism; IoMT; IDS; CNN; LSTM; INTERNET;
D O I
10.3390/electronics12173541
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion.
引用
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页数:32
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