Volume 20, Issue 1 (6-2023)                   JSDP 2023, 20(1): 159-170 | Back to browse issues page


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Mahmoudi-Nasr P, Rahmani A. An Intrusion Detection System for Wireless Body Area Networks. JSDP 2023; 20 (1) : 10
URL: http://jsdp.rcisp.ac.ir/article-1-1108-en.html
University of Mazandaran
Abstract:   (884 Views)
Wireless Body Area Network (WBAN) is a pioneer trend in healthcare technology. Since any cyber-attack on a WBAN could jeopardize the patient's health, securing the WBAN plays a crucial role in healthcare applications. An intrusion detection system (IDS), as a second-line defense, is one of the security methods in computer networks. In this paper, a new IDS has been presented which is able to detect denial of service (DoS) attacks in a WBAN. In the proposed IDS, a genetic algorithm is used to select features of collected data, in a way that increases the performance of the IDS and as a result the WBAN. Then, using support vector machine and k nearest neighbor techniques, the data classification is performed to detect DoS traffic from regular data traffic. Simulation results indicate that the proposed IDS has effective performance with a 90% detection rate.
Article number: 10
Full-Text [PDF 1028 kb]   (329 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2020/01/6 | Accepted: 2023/02/22 | Published: 2023/08/13 | ePublished: 2023/08/13

References
1. [1] M. Ghamari, B. Janko, R. Sherratt, W. Harwin, R. Piechockic, and C. Soltanpur, "A survey on wireless body area networks for ehealthcare systems in residential environments," Sensors, vol. 16, no. 6, p. 831, 2016. [DOI:10.3390/s16060831] [PMID] []
2. [2] M. Contaldo, B. Banerjee, D. Ruffieux, J. Chabloz, E. Le Roux, and C. C. Enz, "A 2.4-GHz BAW-based transceiver for wireless body area networks," IEEE transactions on biomedical circuits and systems, vol. 4, no. 6, pp. 391-399, 2010. [DOI:10.1109/TBCAS.2010.2081363] [PMID]
3. [3] S. Movassaghi, M. Abolhasan, J. Lipman, D. Smith, and A. Jamalipour, "Wireless body area networks: A survey," IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1658-1686, 2014. [DOI:10.1109/SURV.2013.121313.00064]
4. [4] S. Ullah et al., "A comprehensive survey of wireless body area networks," Journal of medical systems, vol. 36, no. 3, pp. 1065-1094, 2012. [DOI:10.1007/s10916-010-9571-3] [PMID]
5. [5] S. Al-Janabi, I. Al-Shourbaji, M. Shojafar, and S. Shamshirband, "Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications," Egyptian Informatics Journal, vol. 18, no. 2, pp. 113-122, 2017. [DOI:10.1016/j.eij.2016.11.001]
6. [6] Mahmoudi-Nasr P, Yazdian Varjani A. "An Access Management System to Mitigate Operational Threats in SCADA System", JSDP 2018; 14 (4) :3-18. [DOI:10.29252/jsdp.14.4.3]
7. [7] M. S. Taha, M. S. M. Rahim, M. M. Hashim, and F. A. Johi, "Wireless body area network revisited," International Journal of Engineering & Technology, vol. 7, no. 4, pp. 3494-3504, 2018.
8. [8] P. Dodangeh and A. H. Jahangir, "A biometric security scheme for wireless body area networks," Journal of Information Security and Applications, vol. 41, pp. 62-74, 2018. [DOI:10.1016/j.jisa.2018.06.001]
9. [9] R. Cavallari, F. Martelli, R. Rosini, C. Buratti, and R. Verdone, "A survey on wireless body area networks: Technologies and design challenges," IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1635-1657, 2014. [DOI:10.1109/SURV.2014.012214.00007]
10. [10] M. Usman, M. R. Asghar, I. S. Ansari, and M. Qaraqe, "Security in Wireless Body Area Networks: From In-Body to Off-Body Communications," IEEE Access, vol. 6, pp. 58064-58074, 2018. [DOI:10.1109/ACCESS.2018.2873825]
11. [11] O. Salem, A. Serhrouchni, A. Mehaoua, and R. Boutaba, "Event Detection in Wireless Body Area Networks using Kalman Filter and Power Divergence," IEEE Transactions on Network and Service Management, 2018. [DOI:10.1109/TNSM.2018.2842195]
12. [12] N. A. Alrajeh, S. Khan, and B. Shams, "Intrusion detection systems in wireless sensor networks: a review," International Journal of Distributed Sensor Networks, vol. 9, no. 5, p. 167575, 2013. [DOI:10.1155/2013/167575]
13. [13] R. Latif, H. Abbas, S. Latif, and A. Masood, "EVFDT: an enhanced very fast decision tree algorithm for detecting distributed denial of service attack in cloud-assisted wireless body area network," Mobile Information Systems, vol. 2015, 2015. [DOI:10.1155/2015/260594]
14. [14] N. K. Jha, A. Raghunathan, and M. Zhang, "Securing medical devices through wireless monitoring and anomaly detection," ed: Google Patents, 2018.
15. [15] G. Thamilarasu and Z. Ma, "Autonomous mobile agent based intrusion detection framework in wireless body area networks," in World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2015 IEEE 16th International Symposium on a, 2015: IEEE, pp. 1-3. [DOI:10.1109/WoWMoM.2015.7158178]
16. [16] Y. Qu, G. Zheng, H. Wu, B. Ji, and H. Ma, "An energy-efficient routing protocol for reliable data transmission in wireless body area networks," Sensors, vol. 19, no. 19, p. 4238, 2019. [DOI:10.3390/s19194238] [PMID] []
17. [17] S. R. Nabavi, N. Osati Eraghi, and J. Akbari Torkestani, "Temperature-Aware Routing in Wireless Body Area Network Based on Meta-Heuristic Clustering Method," Journal of Communication Engineering, 2021.
18. [18] N. Bilandi, H. K. Verma, and R. Dhir, "PSOBAN: a novel particle swarm optimization based protocol for wireless body area networks," SN Applied Sciences, vol. 1, no. 11, pp. 1-14, 2019. [DOI:10.1007/s42452-019-1514-0]
19. [19] B. Vahedian and P. Mahmoudi-Nasr12, "Toward Energy-efficient Communication Protocol in Wireless Body Area Network: A Dynamic Scheduling Policy Approach."
20. [20] M. S. Hajar, M. O. Al-Kadri, and H. K. Kalutarage, "A survey on wireless body area networks: architecture, security challenges and research opportunities," Computers & Security, p. 102211, 2021. [DOI:10.1016/j.cose.2021.102211]
21. [21] M. rabiepour, H. ghasvari and A. khanshan, " Analytical investigation of the capabilities and limitations of using various routing algorithms for use in wireless body area networks ", 3th National Conference on Electrical and Computer Engineering Distributed Systems and Smart Grids, 2016.
22. [22] S. Karchowdhury and M. Sen, "Survey on attacks on wireless body area network," International Journal of Computational Intelligence & IoT, Forthcoming, 2019.
23. [23] S. M. Othman, N. T. Alsohybe, F. M. Ba-Alwi, and A. T. Zahary, "Survey on intrusion detection system types," International Journal of Cyber-Security and Digital Forensics, vol. 7, no. 4, pp. 444-463, 2018.
24. [24] M. Dhuha I., and Sarab M. Hameed. "A feature selection model based on genetic algorithm for intrusion detection." Iraqi Journal of Science, pp. 168-175, 2016.
25. [25] Bamakan, Seyed Mojtaba Hosseini, et al. "An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization." Neurocomputing, Vol. 199,pp. 90-102, 2016. [DOI:10.1016/j.neucom.2016.03.031]
26. [26] P. Hamed Haddad, GholamHossein Dastghaibyfard, and Sattar Hashemi. "Two-tier network anomaly detection model: a machine learning approach." Journal of Intelligent Information Systems, Vol. 48, no.1, pp. 61-74, 2017. [DOI:10.1007/s10844-015-0388-x]
27. [27] Odesile, Adedayo, and Geethapriya Thamilarasu. "Distributed intrusion detection using mobile agents in wireless body area networks." 2017 Seventh International Conference on Emerging Security Technologies (EST). IEEE, 2017. [DOI:10.1109/EST.2017.8090414]
28. [28] Shone, Nathan, et al. "A deep learning approach to network intrusion detection." IEEE transactions on emerging topics in computational intelligence, Vol. 2, no.1, pp. 41-50. 2018. [DOI:10.1109/TETCI.2017.2772792]
29. [29] Woo, Ju-ho, Joo-Yeop Song, and Young-June Choi. "Performance enhancement of deep neural network using feature selection and preprocessing for intrusion detection." 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2019. [DOI:10.1109/ICAIIC.2019.8668995]
30. [30] Newaz, AKM Iqtidar, et al. "Heka: A novel intrusion detection system for attacks to personal medical devices." 2020 IEEE Conference on Communications and Network Security (CNS). IEEE, 2020. [DOI:10.1109/CNS48642.2020.9162311]
31. [31] Hady, Anar A., et al. "Intrusion detection system for healthcare systems using medical and network data: A comparison study." IEEE Access, Vol. 8, pp. 106576-106584, 2020. [DOI:10.1109/ACCESS.2020.3000421]
32. [32] Iwendi, Celestine, et al. "Security of things intrusion detection system for smart healthcare." Electronics Vol. 10, no.12, pp. 1375, 2021. [DOI:10.3390/electronics10121375]
33. [33] Gupta, Karan, et al. "A tree classifier based network intrusion detection model for Internet of Medical Things." Computers and Electrical Engineering, Vol. 102,pp. 108158, 2022. [DOI:10.1016/j.compeleceng.2022.108158]

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