Machine learning-based solutions for securing IoT systems against multilayer attacks

Conference paper


Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Machine learning-based solutions for securing IoT systems against multilayer attacks.
AuthorsAl Sukhni, B., Manna, S., Dave, J. and Zhang, L.
TypeConference paper
Description

With the advancement of technology, IoT systems have been widely used in all sectors from smart homes to healthcare, however, those devices are vulnerable to multilayer security attacks because most of the devices are resource-constrained and hence cannot implement standard security frameworks. In this paper, we have categorised the multilayer IoT attacks and analysed their behaviours. For developing a smart intrusion detection system, three machine learning models (NB, DT and SVM) are trained with three standard IoT datasets (Bot-IoT, ToN-IoT, Edge-IIoTset). To optimize the computational power and the number of features in the training dataset, similar features of multilayer IoT attacks are used for training ML models instead of all features. The achieved accuracy for the NB model is 57%-75% whereas for DT is 93%-100%. A comparison between the two approaches (training with all features and similar features) shows that the accuracy level is better in case of similar features.

KeywordsIoT device; Multilayer attacks; Machine learning; Similar features
Year2022
Conference3rd International Conference on Communication, Networks and Computing
Official URLhttp://event.itmuniversity.ac.in/cnc-2022/
File
File Access Level
Restricted
Web address (URL) of conference proceedingshttps://www.springer.com/series/7899
Publisher's version
File Access Level
Restricted
References

1. Ferrag, M.A., Friha, O., Hamouda, D., Maglaras, L. and Janicke, H., 2022. Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access, 10, pp.40281-40306.
2. Anthi, E., Williams, L., Słowińska, M., Theodorakopoulos, G. and Burnap, P., 2019. A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things Journal, 6(5), pp.9042-9053.
3. Daws, R. (2021) Kaspersky: Attacks on IoT devices double in a year, Internet of Things News. IoT Tech News. Available at: https://www.iottechnews.com/news/2021/sep/07/kaspersky-attacks-on-io... (Accessed: October 31, 2022).
4. S. Khanam, I. B. Ahmedy, M. Y. Idna Idris, M. H. Jaward, and A. Q. Bin Md Sabri, “A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things,” IEEE Access, vol. 8, pp. 219709–219743, 2020.
5. S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of Internet of Things (IoT): A survey,” J. Netw. Comput. Appl., vol. 161, no. 102630, p. 102630, 2020.
6. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for internet of things (IoT) security,” IEEE Commun. Surv. Tutor., vol. 22, no. 3, pp. 1646–1685, 2020.
7. P. Malhotra, Y. Singh, P. Anand, D. K. Bangotra, P. K. Singh, and W.-C. Hong, “Internet of Things: Evolution, concerns and security challenges,” Sensors (Basel), vol. 21, no. 5, p. 1809, 2021.
8. V. Hassija, V. Chamola, V. Saxena, D. Jain, P. Goyal, and B. Sikdar, “A survey on IoT security: Application areas, security threats, and solution architectures,” IEEE Access, vol. 7, pp. 82721–82743, 2019.
9. I. Butun, P. Osterberg, and H. Song, “Security of the internet of things: Vulnerabilities, attacks, and countermeasures,” IEEE Commun. Surv. Tutor., vol. 22, no. 1, pp. 616–644, 2020.
10. IBM (2022) IBM Security X-Force Threat Intelligence Index, Ibm.com. Available at: https://www.ibm.com/reports/threat-intelligence/ (Accessed: November 1, 2022).
11. Rehman, S. ur, Khaliq, M., Imtiaz, S. I., Rasool, A., Shafiq, M., Javed, A. R., Jalil, Z., & Bashir, A. K. (2021). DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU). Future Generations Computer Systems: FGCS, 118, 453–466. https://doi.org/10.1016/j.future.2021.01.022.
12. S. S. Priya, M. Sivaram, D. Yuvaraj, and A. Jayanthiladevi, “Machine Learning based DDOS Detection,” in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020.
13. R. Doshi, N. Apthorpe, and N. Feamster, “Machine learning DDoS detection for consumer Internet of Things devices,” arXiv [cs.CR], 2018.
14. Mukhtar, N. et al. (2020) “Improved hybrid approach for side-channel analysis using efficient convolutional neural network and dimensionality reduction,” IEEE access: practical innovations, open solutions, 8, pp. 184298–184311. doi: 10.1109/access.2020.3029206.
15. M. Zolanvari, M. A. Teixeira, L. Gupta, K. M. Khan, and R. Jain, “Machine learning-based network vulnerability analysis of industrial internet of things,” IEEE Internet Things J., vol. 6, no. 4, pp. 6822–6834, 2019.
16. E. Anthi, L. Williams, M. Slowinska, G. Theodorakopoulos, and P. Burnap, “A supervised intrusion detection system for smart home IoT devices,” IEEE Internet Things J., vol. 6, no. 5, pp. 9042–9053, 2019.
17. R. Ahmad and I. Alsmadi, “Machine learning approaches to IoT security: A systematic literature review,” Internet of Things, vol. 14, no. 100365, p. 100365, 2021.
18. H. F. Atlam and G. B. Wills, IoT Security, Privacy, Safety and Ethics. Cham: Springer International Publishing, 2020, pp. 123–149.
19. A. Mitrokotsa, M. Rieback, and A. Tanenbaum, “Classifying rfid attacks and defenses,” Information Systems Frontiers, vol. 12, pp. 491–505, 11 2010.
20. Z. Ahmad, A. Shahid Khan, C. Wai Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Trans. emerg. telecommun. technol., vol. 32, no. 1, 2021.
21. R. Kumar and R. Sharma, “Leveraging blockchain for ensuring trust in iot: A survey,” Journal of King Saud University - Computer and Information Sciences, 2021.
22. Ferrag, M. A. et al. (2020) “RDTIDS: Rules and decision tree-based intrusion detection system for Internet-of-Things networks,” Future internet, 12(3), p. 44.
23. Manesh, M.R. and Kaabouch, N., 2019. Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions. Computers & Security, 85, pp.386-401.
24. Nawir, M., Amir, A., Yaakob, N. and Lynn, O.B., 2016, August. Internet of Things (IoT): Taxonomy of security attacks. In 2016 3rd international conference on electronic design (ICED) (pp. 321-326). IEEE.
25. Alhowaide, A., Alsmadi, I. and Tang, J. (2021) “Ensemble Detection Model for IoT IDS,” Internet of Things (Netherlands), 16. doi:10.1016/j.iot.2021.100435.
26. A. R. Gad, A. A. Nashat, and T. M. Barkat, “Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset,” IEEE Access, vol. 9, pp. 142206–142217, 2021.

Publication process dates
Deposited05 Dec 2022
Permalink -

https://repository.canterbury.ac.uk/item/93419/machine-learning-based-solutions-for-securing-iot-systems-against-multilayer-attacks

  • 39
    total views
  • 0
    total downloads
  • 18
    views this month
  • 0
    downloads this month

Export as

Related outputs

Investigating the security issues of multi-layer IoT attacks using machine learning techniques
Al Sukhni, B., Dave, J., Manna, S. and Zhang, L. Investigating the security issues of multi-layer IoT attacks using machine learning techniques.
Cyber physical system: Security challenges in Internet of Things system
Mohanta, Bhabendu Kumar, Dehury, Mohan Kumar, Sukhni, Badeea Al and Mohapatra, Niva 2022. Cyber physical system: Security challenges in Internet of Things system. in: 2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) IEEE.
Investigating the security issues of multi-layer IoMT attacks using machine learning techniques
Al Sukhni, B., Manna, S., Dave, J. and Zhang, L. 2022. Investigating the security issues of multi-layer IoMT attacks using machine learning techniques.
Designing a system to mimic expert cognition: An initial prototype
Hepenstal, Sam, Zhang, Leishi and William Wong, B. L. 2022. Designing a system to mimic expert cognition: An initial prototype. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 66 (1), pp. 2057-2061. https://doi.org/10.1177/1071181322661092
Accuracy and repeatability study of an elbow exoskeleton for multistage exercises
Manna, Soumya K 2022. Accuracy and repeatability study of an elbow exoskeleton for multistage exercises. in: 2022 20th International Conference on Mechatronics - Mechatronika (ME) IEEE.
A smart and home-based telerehabilitation tool for patients with neuromuscular disorder
Manna, S., Azhar, H., Smith, D. and Islam, T. 2022. A smart and home-based telerehabilitation tool for patients with neuromuscular disorder.
A qualitative review of educational robots for STEM: Technical features and impacts
Manna, S. and Azhar, H. 2022. A qualitative review of educational robots for STEM: Technical features and impacts.
Evaluation of students’ performance in CDIO projects through blended learning
Manna, S., Battikh, N., Nortcliffe, A. and Camm, J. 2022. Evaluation of students’ performance in CDIO projects through blended learning.
A smart and secure IoMT tele-neurorehabilitation framework for post-stroke patients
Manna, S., Azhar, H. and Sakel, M. 2022. A smart and secure IoMT tele-neurorehabilitation framework for post-stroke patients. in: Bhaumik, S., Chattopadhyay, S., Chattopadhyay, T. and Bhattacharya, S. (ed.) Proceedings of International Conference on Industrial Instrumentation and Control ICI2C 2021 Singapore Springer. pp. 11-20
Enhancing hands-on skills under capstone CDIO project using blended learning approach
Manna, S., Battikh, N. and Camm, J. 2021. Enhancing hands-on skills under capstone CDIO project using blended learning approach . Sheffield
An analysis of expertise in intelligence analysis to support the design of human-centered artificial intelligence
Hepenstal, S., Zhang, L. and Wong, BL William 2021. An analysis of expertise in intelligence analysis to support the design of human-centered artificial intelligence. https://doi.org/10.1109/SMC52423.2021.9659095
Automated identification of insight seeking behaviours, strategies and rules: a preliminary study
Hepenstal, S., Zhang, L. and Wong, BL William 2021. Automated identification of insight seeking behaviours, strategies and rules: a preliminary study. Sage Journals: Proceedings of the Human Factors and Ergonomics Society Annual Meeting . (65), pp. 1269-1273. https://doi.org/https://doi.org/10.1177/1071181321651348
An inclusive student-led online class test during the pandemic
Manna, S. and Azhar, H. 2021. An inclusive student-led online class test during the pandemic . Assessment and Feedback Symposium 2021.
A granular computing approach to provide transparency of intelligent systems for criminal investigations
Zhang, L. 2021. A granular computing approach to provide transparency of intelligent systems for criminal investigations. in: Pedrycz, W. and Chen, S.-M. (ed.) Interpretable Artificial Intelligence: A Perspective of Granular Computing Cham Springer.
Developing conversational agents for use in criminal investigations
Hepenstal, S., Zhang, L., Kodagoda N. and Wong B.L.W 2021. Developing conversational agents for use in criminal investigations. ACM Transactions on Interactive Intelligent Systems. 11 (3-4), pp. 1-35. https://doi.org/10.1145/3444369
Adaptive and flexible online learning during Covid19 lockdown
Manna, S., Nortcliffe, A., Sheikholeslami, G. and Richmond-Fuller, A. 2021. Adaptive and flexible online learning during Covid19 lockdown.
Developing engineering growth mindset through CDIO outreach activites
Manna, S., Nortcliffe, A. and Sheikholeslami, G. 2020. Developing engineering growth mindset through CDIO outreach activites. Gothenburg, Sweden http://www.cdio.org/.
Design of a game-based rehabilitation system using Kinect sensor
Manna, S. and Dubuy, V. N. 2019. Design of a game-based rehabilitation system using Kinect sensor. Minneapolis, MN, USA ASME.
Assessment of joint parameters in a Kinect sensor based rehabilitation game
Manna, S. and Dubey, V. N. 2019. Assessment of joint parameters in a Kinect sensor based rehabilitation game. Anaheim, California, USA ASME.
Rehabilitation strategy for post-stroke recovery using an innovative elbow exoskeleton
Manna, S. and Dubey, V. N. 2019. Rehabilitation strategy for post-stroke recovery using an innovative elbow exoskeleton. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 233 (6), pp. 668-680. https://doi.org/10.1177/0954411919847058
A portable elbow exoskeleton for three stages of rehabilitation
Manna, S. and Dubey, V. N. 2019. A portable elbow exoskeleton for three stages of rehabilitation. Journal of Mechanisms and Robotics. 11 (6), p. 065002. https://doi.org/10.1115/1.4044535