International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

A Machine Learning Framework for Automatic Detection and Classification of Cyber Attacks in IoT Use Cases

  • Hussam Aleem Mohammed (Computer Engineering Department, College of Computer and Information Systems, Umm Al-Qura University) ;
  • Amar Yusof Jaffar (Computer Engineering Department, College of Computer and Information Systems, Umm Al-Qura University)
  • Received : 2025.01.05
  • Published : 2025.01.30
Download PDF
⟨ Previous Next ⟩

Abstract

Internet of Things (IoT) use cases are vulnerable to cyber-attacks due to lack of global standards and involvement of heterogeneous devices, protocols and platforms. Traditional methods are found inadequate safeguard IoT applications. With the emergence of Artificial Intelligence (AI), machine learning (ML) and deep learning techniques are widely used to solve security problems in different applications. Learning capability of AI models paves way for intelligent solutions. In this paper, we proposed a ML framework for automatic detection and classification of cyber-attacks in IoT use cases. We proposed a hyperparameter optimization method, designed for optimization of parameters of four ML techniques in tune with the dataset, used in the proposed framework. An algorithm named Learning based Optimal Machine Learning for Cyber Attack Detection and Classification (LbOML-CADC) is also proposed. This algorithm exploits hyperparameter tuning method for efficient detection and classification of cyber-attacks. We evaluated our framework using UNSW-NB15 dataset. Our empirical study reveals that highest accuracy achieved is 97.59%.

Keywords

References

  1. Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Diaa-Eldin A. Mansour; Matti Lehtonen and Mohamed M. F. Darwish; (2021). Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning . IEEE Access. http://doi:10.1109/ACCESS.2021.3083499
  2. MINH-QUANG TRAN, MAHMOUD ELSISI, KARARMAHMOUD , MENG-KUN LIU, MATTI LEHTONEN AND MOHAMED M. F. DARWISH. (2021). Experimental Setup for Online Fault Diagnosis of Induction Machines via Promising IoT and Machine Learning: Towards Industry 4.0 Empowerment. IEEE. 9, pp.115429-115441. http://doi:10.1109/ACCESS.2021.3105297
  3. Pallavi Arora; Baljeet Kaur and Marcio Andrey Teixeira; (2021). Evaluation of Machine Learning Algorithms Used on Attacks Detection in Industrial Control Systems . Journal of The Institution of Engineers (India): Series B. http://doi:10.1007/s40031-021-00563-z
  4. Hasan, Mahmudul; Milon Islam, Md.; Islam, Ishrak and Hashem, M.M.A. (2019). Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches. Internet of Things, 100059–. http://doi:10.1016/j.iot.2019.100059
  5. Aboelwafa, Mariam M. N.; Seddik, Karim G.; Eldefrawy, Mohamed H.; Gadallah, Yasser and Gidlund, Mikael (2020). A Machine Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT. IEEE Internet of Things Journal, 1–1. http://doi:10.1109/JIOT.2020.2991693
  6. YAKUB KAYODE SAHEED and AND MICHEALOLAOLU AROWOLO. (2021). Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Netw. IEEE. 9, pp.161546-161554. http://doi:10.1109/ACCESS.2021.3128837
  7. Ahmad Ali AlZubi; Mohammed Al-Maitah and Abdulaziz Alarifi; (2021). Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques . Soft Computing. http://doi:10.1007/s00500-021-05926-8
  8. Shafiq, Muhammad; Tian, Zhihong; Bashir, Ali Kashif; Du, Xiaojiang and Guizani, Mohsen (2020). CorrAUC: a Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine Learning Techniques. IEEE Internet of Things Journal, 1–1. http://doi:10.1109/JIOT.2020.3002255
  9. Rahman, Md Arafatur; Asyharia, A. Taufiq; Leong, L.S.; Satrya, G.B.; Tao, M. Hai and Zolkipli, M.F. (2020). Scalable Machine Learning-Based Intrusion Detection System for IoT-Enabled Smart Cities. Sustainable Cities and Society, 102324–. http://doi:10.1016/j.scs.2020.102324
  10. Pheeha Machaka, Olasupo Ajayi, Hloniphani Maluleke, Ferdinand Kahenga, Antoine Bagula, Kyandoghere Kyamakya. (2022). Modelling DDoS Attacks in IoT Networks Using Machine Learning. Springer., pp.1-20.
  11. Abhishek Verma and Virender Ranga. (2019). Machine Learning Based Intrusion Detection Systems for IoT Applications. Springer., pp.1-24. https://doi.org/10.1007/s11277-019-06986-8
  12. Maede Zolanvari, Marcio A. Teixeira, Lav Gupta, Khaled M. Khan and Raj Jain. (2019). Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things. IEEE. 6(4), pp.1-13. http://DOI:10.1109/JIOT.2019.2912022
  13. Adi, Erwin; Anwar, Adnan; Baig, Zubair and Zeadally, Sherali (2020). Machine learning and data analytics for the IoT. Neural Computing and Applications. http://doi:10.1007/s00521-020-04874-y
  14. Hussain, Fatima; Hussain, Rasheed; Hassan, Syed Ali and Hossain, Ekram (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges. IEEE Communications Surveys & Tutorials, 1–1. http://doi:10.1109/COMST.2020.2986444
  15. Geetha, R. and Thilagam, T. (2020). A Review on the Effectiveness of Machine Learning and Deep Learning Algorithms for Cyber Security. Archives of Computational Methods in Engineering,. http://doi:10.1007/s11831-020-09478-2
  16. Liang, Fan; Hatcher, William G.; Liao, Weixian; Gao, Weichao and Yu, Wei (2019). Machine Learning for Security and the Internet of Things: the Good, the Bad, and the Ugly. IEEE Access, 1–1. http://doi:10.1109/access.2019.2948912
  17. Mohanta, B. K., Jena, D., Satapathy, U., & Patnaik, S. (2020). Survey on IoT Security:Challenges and Solution using Machine Learning, Artificial Intelligence and Blockchain Technology. Internet of Things, 100227. http://doi:10.1016/j.iot.2020.100227
  18. da Costa, Kelton A.P.; Papa, João P.; Lisboa, Celso O.; Munoz, Roberto and de Albuquerque, Victor Hugo C. (2019). Internet of Things: A Survey on Machine Learning-based Intrusion Detection Approaches. Computer Networks, S1389128618308739–. http://doi:10.1016/j.comnet.2019.01.023
  19. Shereen Ismail, Diana Dawoud and Hassan Reza. (2022). Machine learning techniques for Detection of Cyber Attacks in IoT Use Cases. IEEE. .(.), pp.0481-0486.
  20. Iqbal H. Sarker,Asif Irshad Khan,Yoosef B. Abushark and Fawaz Alsolami. (2022). Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Direction. Springer, p.296–312. http://doi:10.20944/preprints202203.0087.v1
  21. Hao Xu, Zihan Sun, Yuan Cao and Hazrat Bilal. (2023). A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Springer, pp.1-13. https://doi.org/10.1007/s00500-023-09037-4
  22. Hemantha Krishna Bharadwaj; Aayush Agarwal;Vinay Chamola; Naga Rajiv Lakkaniga; Vikas Hassija; Mohsen Guizani and Biplab Sikdar; (2021). A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications . IEEE Access. http://doi:10.1109/access.2021.3059858
  23. Elena Becker, Maanak Gupta and Kshitiz Aryal. (2023). Using Machine Learning for Detection and Classification of Cyber Attacks in Edge IoT. IEEE, pp.1-11. http://DOI:10.1109/EDGE60047.2023.00063
  24. Hao Xu, Zihan Sun, Yuan Cao and Hazrat Bilal. (2023). A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Springer., p.14469–14481. https://doi.org/10.1007/s00500-023-09037-4
  25. Shaukat, Kamran; Luo, Suhuai; Varadharajan, Vijay; Hameed, Ibrahim A. and Xu, Min (2020). A Survey on Machine Learning Techniques for Cyber Security in the Last Decade. IEEE Access, 8, 222310–222354. http://doi:10.1109/access.2020.3041951
  26. Prabhat Kumar; Govind P. Gupta and Rakesh Tripathi; (2021). Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks . Arabian Journal for Science and Engineering. http://doi:10.1007/s13369-020-05181-3
  27. Skowron, Monika; Janicki, Artur and Mazurczyk, Wojciech (2020). Traffic Fingerprinting Attacks on Internet of Things using Machine Learning. IEEE Access, 1–1. http://doi:10.1109/ACCESS.2020.2969015
  28. Chhabra, Gurpal Singh; Singh, Varinder Pal and Singh, Maninder (2018). Cyber forensics framework for big data analytics in IoT environment using machine learning. Multimedia Tools and Applications. http://doi:10.1007/s11042-018-6338-1
  29. Eirini Anthi; Lowri Williams; Amir Javed and Pete Burnap; (2021). Hardening machine learning denial of service (DoS) defences against adversarial attacks in IoT smart home networks . Computers & Security. http://doi:10.1016/j.cose.2021.102352
  30. Li, Fangyu; Shinde, Aditya; Shi, Yang; Ye, Jin; Li, Xiang-Yang and Song, Wen Zhan (2019). System Statistics Learning-Based IoT Security: Feasibility and Suitability. IEEE Internet of Things Journal, 1–1. http://doi:10.1109/JIOT.2019.2897063
  31. Moustafa, Nour, and Jill Slay. "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)." Military Communications and Information Systems Conference (MilCIS), 2015. IEEE, 2015.
  32. Moustafa, Nour, and Jill Slay. "The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 dataset and the comparison with the KDD99 dataset." Information Security Journal: A Global Perspective (2016): 1-14. https://doi.org/10.1080/19393555.2015.1125974
  33. Moustafa, Nour, et al. . "Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks." IEEE Transactions on Big Data (2017).
  34. Nelder, J. A.; Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7(4), 308–313. http://doi:10.1093/comjnl/7.4.308.
  35. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220 (4598):671–680, 1983. https://doi.org/10.1126/science.220.4598.671
  36. M. J. D. Powell. A direct search optimization method that models the objective and constraint functions by linear interpolation. Advances in Optimization and Numerical Analysis, pages 51– 67, 1994. http://doi:10.1007/978-94-015-8330-5_4.
  37. UNSW-NB dataset. Retrieved from https://www.kaggle.com/datasets/mrwellsdavid/unsw-nb15