Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview

Computer and Knowledge Engineering

دوره 7، شماره 2 - شماره پیاپی 14، اسفند 2024، صفحه 65-74   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/cke.2024.89572.1127

نویسندگان

Peyman Vafadoost Sabzevar¹؛ Hamidreza Rokhsati^{* 2}؛ Alireza Chamansara³؛ Ahmad Hajipour⁴

¹Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran.

²Department of Computer, Control and Management Engineering, Sapienza University, Rome, Italy.

³Department of Biomedical Engineering, Materials and Energy Research Center, Tehran, Iran.

⁴Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran.

چکیده

In today's era, the Internet of Things has become one of the important pillars in organizations, hospitals, and research circles and is recognized as an integral part of the Internet. One of the important areas that require online monitoring is medical imaging equipment, whose functional information is transmitted through the Internet of Things. Server security and intrusion prevention, along with anomaly detection, are critical requirements for these networks. The purpose of anomaly detection is to develop methods that can detect attackers' attacks and prevent them from happening again. Algorithms and methods based on statistics play an important role in predicting and diagnosing anomalies. In this article, the isolation forest algorithm was used for training on 80% of the dataset related to the data of the Internet of Medical Things network, and then this model was tested and evaluated on the remaining 20%. The results show 90.54% accuracy in detecting anomalies in the received data, which confirms the effective performance of this method in this field.

کلیدواژه‌ها

Anomaly؛ Detection Anomaly؛ Internet of Medical Things؛ Isolation Forest؛ Unsupervised Learning

مراجع

1. Chun, S. Weber and H. Tewari, "A Lightweight Encryption Scheme for IoT Devices in the Fog," Proceedings of the Future Technologies Conference, 2022, pp. 147-161.
2. Singh, Davinder, "Internet of things," Factories of the Future: Technological Advancements in the Manufacturing Industry, 2023, ch. 9, pp. 195-227.
3. M. Salim et al. and J. H. Park. (2021, Sep.). Homomorphic encryption based privacy-preservation for iomt. Applied Sciences. [Online]. 11(18), p. 8757. Available: 10.3390/app11188757
4. Mazi, F. N. Arsene and A. M. Dissanayaka, "The influence of black market activities through dark web on the economy: a survey," In The Midwest Instruction and Computing Symposium.(MICS), Milwaukee School of Engineering and Northwestern Mutual, Milwaukee, Wisconsin, 2020.
5. Gandhi, T. Poongodi and K. S. Kumar. (2024). Original Research Article Enhancing data security of cardiac patients in IoMT with Twin-Shield Encryption. Journal of Autonomous Intelligence. [Online]. 7(2). Available: 10.32629/jai.v7i2.1322
6. T. Kadhim, A. M. Alsahlany, S. M. Wadi, H. T. Kadhum. (2020, May.). An Overview of Patient’s Health Status Monitoring System Based on Internet of Things (IoT). Wireless Personal Communications. [Online]. 114(3), pp. 2235-2262. Available: 10.1007/s11277-020-07474-0
7. Rghioui and A. Oumnad. (2018, Oct.). Challenges and Opportunities of Internet of Things in Healthcare. International Journal of Electrical & Computer Engineering. [Online]. 8(5), pp. 2753-2761. Available: 10.11591/ijece.v8i5.pp2753-2761
8. Kasinathan et al. and M. A. Spirito, "An IDS framework for internet of things empowered by 6LoWPAN" Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security, 2013, pp. 1337-1340.
9. Xie. S. Han, B. Tian and S. Parvin. (2011, Jul.). Anomaly detection in wireless sensor networks: A survey. Journal of Network and computer Applications.[Online]. 34(4), pp. 1302-1325. Available: 10.1016/j.jnca.2011.03.004
10. Rajasegarar, C. Leckie and M. Palaniswami. (2008, Aug.). Anomaly detection in wireless sensor networks. IEEE Wireless Communications. [Online]. 15(4), pp. 34-40. Available: 10.1109/MWC.2008.4599219
11. Behniafar, A. Nowroozi and H. R. Shahriari. (2018, Jul.). A Survey of Anomaly Detection Approaches in Internet of Things. IseCure.[Online]. 10(2). Available: sid.ir
12. CyberMDX, "2020 Vision: A Review of Major IT & Cyber Security Issues Affecting" Healthcare, 2020.
13. MADDOX, "Why Medical Data is 50 Times More Valuable Than a Credit Card," Healthcare, 2020.
14. Ghubaish, T. Salman, M. Zolanvari, D. Unal, A. Al-Ali and R. Jain. (2020, Dec.). Recent advances in the internet-of-medical-things (IoMT) systems security. IEEE Internet of Things Journal.[Online]. 8(11), pp. 8707-8718. Available: 10.1109/JIOT.2020.3045653
15. Zachos et al. and J. Rodriguez. (2021, Oct.). An anomaly-based intrusion detection system for internet of medical things networks. Electronics. [Online]. 10(21), p. 2562. Available: 10.3390/electronics10212562
16. S. Mohamed, S. Aydin, A. Alkhayyat and R. Q. Malik. (2022, Jul). Kalman and Cauchy clustering for anomaly detection based authentication of IoMTs using extreme learning machine. IET Communications. [Online].  Available: 10.1049/cmu2.12467
17. Alsalman. (2024, Jan.). A Comparative Study of Anomaly Detection Techniques for IoT Security using AMoT (Adaptive Machine Learning for IoT Threats). IEEE Access. [Online]. 12, pp. 14719-14730. Available: 10.1109/ACCESS.2024.3359033
18. Wang et al. and K. Zhong. (2022, Dec.). Anomaly detection in Internet of medical Things with Blockchain from the perspective of deep neural network. Information Sciences. [Online]. 617, pp. 133-149. Available: 10.1016/j.ins.2022.10.060
19. H. Wagan et al. and D. R. Shin. (2023, Jan.). A fuzzy-based duo-secure multi-modal framework for IoMT anomaly detection. Journal of King Saud University-Computer and Information Sciences. [Online]. 35(1), pp. 131-144. Available: 10.1016/j.jksuci.2022.11.007
20. B. Awotunde et al. and R. G. Jimoh, "A deep learning-based intrusion detection technique for a secured IoMT system," International Conference on Informatics and Intelligent Applications, Cham: Springer International Publishing, 2021, pp. 50-62.
21. Kumar, G. P. Gupta and R. Tripathi. (2021, Jan.). An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks. Computer Communications. [Online]. 166, pp. 110-124. Available: 10.1016/j.comcom.2020.12.003
22. Tavallaee, E. Bagheri, W. Lu and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," IEEE symposium on computational intelligence for security and defense applications, Ottawa, ON, Canada, 2009, pp. 1-6.
23. Dhanabal and S. P. Shantharajah. (2015, Jun.). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International journal of advanced research in computer and communication engineering. [Online]. 4(6), pp. 446-452. Available: 10.17148/IJARCCE.2015.4696
24. Su, H. Sun, J. Zhu, S. Wang and Y. Li. (2020, Feb.). BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE. [Online]. 8, pp. 29575-29585. Available: 10.1109/ACCESS.2020.2972627
25. Kavitha and N. U. Maheswari. (2021, Mar.). Network anomaly detection for NSL-KDD dataset using deep learning. Information Technology. [Online]. 9(2), pp. 821-827.
26. Xu and F. Sabrina. (2021, Sep.). Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset. IEEE. [Online]. 9, pp. 140136-140146. Available: 10.1109/ACCESS.2021.3116612
27. Türk. (2023, Jun.). Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. [Online]. 12(2), pp. 465-477. Available: 10.17798/bitlisfen.1240469
28. S. Bhuvaneshwari et al. and P. Prabu. (2022, Jul.). Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier. Computers, Materials & Continua. [Online]. 70(3). Available: 10.32604/cmc.2022.020769
29. S. Kaushik and P. R. Deshmukh. (2011). Detection of attacks in an intrusion detection system. International Journal of Computer Science and Information Technologies (IJCSIT). [Online]. 2(3), pp. 982-986. Available: citeseerx.ist.psu.edu
30. Mahesh. (2020, Jan.). Machine learning algorithms-a review. International Journal of Science and Research (IJSR). [Online]. 9(1), pp. 381-386. Available: 10.21275/ART20203995
31. T. Liu, K. M. Ting and Zhi-Hua Zhou, "Isolation forest," eighth ieee international conference on data mining, Pisa, Italy, 2008, pp. 413-422.
32. Abe, B. Zadrozny and J. Langford, "Outlier detection by active learning," Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 504-509.
33. He, X. Xu and S. Deng. (2003, Jun.). Discovering cluster-based local outliers. Pattern recognition letters. [Online]. 24(9-10), pp. 1641-1650. Available: 10.1016/S0167-8655(03)00003-5
34. Al-Qatf, Y. Lasheng, M. Al-Habib and K. Al-Sabahi. (2018, Sep.). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. Ieee Access. [Online]. 6, pp. 52843-52856. Available: 10.1109/ACCESS.2018.2869577
35. Ieracitano et al. and A. Hussain. (2020, Apr.). A novel statistical analysis and autoencoder driven intelligent intrusion detection approach. Neurocomputing. [Online].  387, pp. 51-62. Available: 10.1016/j.neucom.2019.11.016
36. Javaid, Q. Niyaz, W. Sun and M. Alam, “A deep learning approach for network intrusion detection system,” Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), 2016, pp. 21-26.