Abstract
IoT systems are prone to security attacks from several IoT layers as most of them possess limited resources and are unable to implement standard security protocols. This paper distinguishes multilayer IoT attacks from single-layer attacks and investigates their functioning. For developing a robust and efficient IDS (intrusion detection system), we have trained a few machine learning (ML) approaches such as NB, DT, and SVM using three standard sets of IoT datasets (Bot-IoT, ToN-IoT, Edge-IIoTset). Instead of using all features, the ML models are trained with similar features of multilayer IoT attacks to use optimal computational power and minimum number of features in the training dataset. The NB model achieves an accuracy of 57%–75%, while the DT model achieves an accuracy of 93%–100%. The outcome of the two ML models reveals that training with similar features possesses a higher accuracy level.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ferrag, M.A., Friha, O., Hamouda, D., Maglaras, L., Janicke, H.: Edge-IIoTset: a new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access 10, 40281–40306 (2022)
Anthi, E., Williams, L., Słowińska, M., Theodorakopoulos, G., Burnap, P.: A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J. 6(5), 9042–9053 (2019)
Daws, R.: Kaspersky: Attacks on IoT devices double in a year, Internet of Things News. IoT Tech News (2021). https://www.iottechnews.com/news/2021/sep/07/kaspersky-attacks-on-iot-devices-double-in-a-year/. Accessed Oct 31 2022
Khanam, S., Ahmedy, I.B., Idna Idris, M.Y., Jaward, M.H., Bin Md Sabri, A.Q.: A survey of security challenges, attacks taxonomy and advanced countermeasures in the internet of things. IEEE Access, 8, 219709–219743 2020
Tahsien, S.M., Karimipour, H., Spachos, P.: Machine learning based solutions for security of internet of things (IoT): a survey. J. Netw. Comput. Appl. 161(102630), 102630 (2020)
Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun. Surv. Tutor. 22(3), 1646–1685 (2020)
Malhotra, P., Singh, Y., Anand, P., Bangotra, D.K., Singh, P.K., Hong, W.-C.: Internet of things: evolution, concerns and security challenges. Sensors (Basel) 21(5), 1809 (2021)
Hassija, V., Chamola, V., Saxena, V., Jain, D., Goyal, P., Sikdar, B.: A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7, 82721–82743 (2019)
Butun, I., Osterberg, P., Song, H.: Security of the internet of things: vulnerabilities, attacks, and countermeasures. IEEE Commun. Surv. Tutor. 22(1), 616–644 (2020)
IBM. IBM Security X-Force Threat Intelligence Index, Ibm.com. Available at: https://www.ibm.com/reports/threat-intelligence/ (Accessed: November 1, 2022)
ur Rehman, S., et al.: DIDDOS: an approach for detection and identification of distributed denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU). Future Gener. Comput. Syst.118, 453–466 (2021). https://doi.org/10.1016/j.future.2021.01.022
Priya, S.S., Sivaram, M., Yuvaraj, D., Jayanthiladevi, A.: Machine learning based DDOS detection. In: 2020 International Conference on Emerging Smart Computing and Informatics (ESCI) (2020)
Doshi, R., Apthorpe, N., Feamster, N.: Machine learning DDoS detection for consumer Internet of Things devices, arXiv [cs.CR] (2018)
Mukhtar, N., et al.: Improved hybrid approach for side-channel analysis using efficient convolutional neural network and dimensionality reduction. IEEE Access: Pract. Innovations, Open Solutions 8, 184298–184311 (2020). https://doi.org/10.1109/access.2020.3029206
Gad, A.R., Nashat, A.A., Barkat, T.M.: Intrusion detection system using machine learning for vehicular ad hoc networks based on ToN-IoT dataset. IEEE Access 9, 142206–142217 (2021)
Zolanvari, M., Teixeira, M.A., Gupta, L., Khan, K.M., Jain, R.: Machine learning-based network vulnerability analysis of industrial internet of things. IEEE Internet Things J. 6(4), 6822–6834 (2019)
Ahmad, R., Alsmadi, I.: Machine learning approaches to IoT security: a systematic literature review. Internet of Things 14(100365), 100365 (2021)
Atlam, H.F., Wills, G.B.: IoT Security, Privacy, Safety and Ethics, pp. 123–149. Springer, Cham (2020)
Mitrokotsa, A., Rieback, M.R., Tanenbaum, A.S.: Classifying RFID attacks and defenses. Inf. Syst. Front. 12, 491–505 (2010)
Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021)
Kumar, R., Sharma, R.: Leveraging blockchain for ensuring trust in iot: a survey. J. King Saud Univ. Comput. Inf. Sci. 34(10), 8599–8622 (2022)
Ferrag, M.A., et al.: RDTIDS: rules and decision tree-based intrusion detection system for Internet-of-Things networks. Future internet 12(3), 44 (2020)
Manesh, M.R., Kaabouch, N.: Cyber-attacks on unmanned aerial system networks: detection, countermeasure, and future research directions. Comput. Secur. 85, 386–401 (2019)
Nawir, M., Amir, A., Yaakob, N. Lynn, O.B.: Internet of Things (IoT): taxonomy of security attacks. In: 2016 3rd International Conference on Electronic Design (ICED), pp. 321–326. IEEE 2016
Alhowaide, A., Alsmadi, I., Tang, J.: Ensemble detection model for IoT IDS. Internet of Things (Netherlands) 16, 100435 (2021). https://doi.org/10.1016/j.iot.2021.100435
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Al Sukhni, B., Manna, S.K., Dave, J.M., Zhang, L. (2023). Machine Learning-Based Solutions for Securing IoT Systems Against Multilayer Attacks. In: Tomar, R.S., et al. Communication, Networks and Computing. CNC 2022. Communications in Computer and Information Science, vol 1893. Springer, Cham. https://doi.org/10.1007/978-3-031-43140-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-43140-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43139-5
Online ISBN: 978-3-031-43140-1
eBook Packages: Computer ScienceComputer Science (R0)