An Enhanced Intrusion Detection Classification Approach for Securing IoT Networks
DOI:
https://doi.org/10.26713/cma.v16i2.3008Keywords:
Intrusion detection, Internet of Things, Classification, Cybersecurity, Data breachesAbstract
The research investigates different classification methods that IDS developers use for developing intrusion detection systems. Recent rapid growth of internet of things (IoT) devices created a massive surge in available data requiring highly effective methods for preventing malicious activities. The research attempts to boost IDS effectiveness through ML algorithm implementation to obtain precise intrusion classification and identification. The research group obtained assessment data through three datasets which included IoT-Modbus and IoT-Fridge and IoT-Weather to measure classification frameworks’ abilities when detecting different threats affecting IoT systems. For the IoTFridge dataset, the one-vs-one (OvO) classification reached 100% accuracy; for the IoT-Weather and IoT-Modbus, the accuracy was 99.7% and 77.62%, respectively. The one-vs-rest (OvR) classification method yielded accuracies of 100% for IoT-Fridge data, 98.02% for IoT-Weather, and 77.62% for IoT-Modbus. The performance results on IoT-Modbus and IoT-Fridge datasets were comparable between OvO and OvR methods while OvO produced slightly superior results on the IoT-Weather dataset. The research demonstrates that multi-class classification techniques demonstrate outstanding performance for IDS systems which can boost IoT application cybersecurity capabilities. The major objective of this research is to introduce a new IDS system with enhanced potential of detecting threats in IoT environments, while handling specific IoT protection obstacles.
Downloads
References
H. Alaiz-Moreton, J. Aveleira-Mata, J. Ondicol-Garcia, A. L. Muñoz-Castañeda, I. García and C. Benavides, Multiclass classification procedure for detecting attacks on MQTT-IoT protocol, Complexity 2019(1) (2019), 6516253, DOI: 10.1155/2019/6516253.
M. Al-Boghdady, M. El-Ramly and K. Wassif, iDetect for vulnerability detection in Internet of Things operating systems using machine learning, Scientific Reports 12 (2022), Article number: 17086, DOI: 10.1038/s41598-022-21325-x.
B. K. Alotaibi, F. A. Khan, Y. Qawqzeh, G. Jeon and D. Camacho, Performance and communication cost of deep neural networks in federated learning environments: An empirical study, International Journal of Interactive Multimedia and Artificial Intelligence 9(4) (2024), 6 – 17, DOI: 10.9781/ijimai.2024.12.001.
A. Alsaedi, N. Moustafa, Z. Tari, A. Mahmood and A. Anwar, TON_IoT telemetry dataset: A new generation dataset of IoT and IIoT for data-driven intrusion detection systems, IEEE Access 8 (2020), 165130 – 165150, DOI: 10.1109/ACCESS.2020.3022862.
A. Azab, M. Khasawneh, S. Alrabaee, K.-K. R. Choo and M. Sarsour, Network traffic classification: Techniques, datasets, and challenges, Digital Communications and Networks 10(3) (2022), 676 – 692, DOI: 10.1016/j.dcan.2022.09.009.
T. Booij and I. Chiscop, Statistical analysis of ToN_IoT datasets, IEEE DataPort (2021), DOI: 10.21227/frw4-sk06.
A. Churcher, R. Ullah, J. Ahmad, S. ur Rehman, F. Masood, M. Gogate, F. Alqahtani, B. Nour and W. J. Buchanan, An experimental analysis of attack classification using machine learning in IoT networks, Sensors 21(2) (2021), 446, DOI: 10.3390/s21020446.
A. Dahou, M. A. Elaziz, S. A. Chelloug, M. A. Awadallah, M. A. Al-Betar and M. A. A. Al-qaness and A. Forestiero, Intrusion detection system for IoT based on deep learning and modified reptile search algorithm, Computational Intelligence and Neuroscience 2022 (2022), Article ID 6473507, DOI: 10.1155/2022/6473507.
O. Elnakib, E. Shaaban, M. Mahmoud and K. Emara, EIDM: Deep learning model for IoT intrusion detection systems, The Journal of Supercomputing 79 (2023), 13241 – 13261, DOI: 10.1007/s11227-023-05197-0.
A. Kaushik and H. Al-Raweshidy, A novel intrusion detection system for Internet of Things devices and data, Wireless Networks 30 (2024), 285 – 294, DOI: 10.1007/s11276-023-03435-0.
F. A. Khan, A. Rahman, M. Alharbi and Y. K. Qawqzeh, Awareness and willingness to use PHR: A roadmap towards cloud-dew architecture based PHR framework, Multimedia Tools and Applications 79 (2020), 8399 – 8413, DOI: 10.1007/s11042-018-6692-z.
T.-T.-H. Le, Y. E. Oktian and H. Kim, XGBoost for imbalanced multi-class classification based Internet of Things intrusion detection systems, Sustainability 14(14) (2022), 8707, DOI: 10.3390/su14148707.
R. Priya, N. Suman, A. Utsav and A. Abhishek, Internet of Things (IoTs) - Review and it’s multiple classification, in: Proceedings of the Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV, Tirunelveli, India, 2021), pp. 328 – 334 (2021), DOI: 10.1109/ICICV50876.2021.9388464.
R. Qaddoura, A. M. Al-Zoubi, H. Faris and I. Almomani, A multi-layer classification approach for intrusion detection in IoT networks based on deep learning, Sensors 21(9) (2021), 2987, DOI: 10.3390/s21092987.
Y. K. Qawqzeh and M. Ashraf, A fraud detection system using decision tree classification in online transactions, in: Proceedings of the 12th International Conference on Software and Computer Applications (ICSCA 2023, Kuantan, Malaysia, 2023), pp. 1 – 8 (2023), DOI: 10.1145/3587828.3587860.
Y. Qawqzeh and W. Samaraa, Deep learning-based multiclass anomaly detection in the IoT_GPS_Tracker dataset: Unveiling patterns for enhanced tracking accuracy, in: Proceedings of the 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT, Faridabad, India, 2023), pp. 687 – 690 (2023), DOI: 10.1109/ICAICCIT60255.2023.10466099.
Y. K. Qawqzeh, A. Alourani and S. Ghwanmeh, An improved breast cancer classification method using an enhanced AdaBoost classifier, International Journal of Advanced Computer Science and Applications 14(1) (2023), 473 – 478, DOI: 10.14569/IJACSA.2023.0140151.
Y. Qawqzeh, M. B. I. Reaz, M. A. M. Ali, K. B. Gan, S. Zulkifli and A. Noraidatulakma, Assessment of atherosclerosis in erectile dysfunction subjects using second derivative of photoplethysmogram, Scientific Research and Essays 7(25) (2012), 2230 – 2236, URL: https://academicjournals.org/journal/SRE/article-abstract/898213227600.
D. Rani, N. S. Gill, P. Gulia and J. M. Chatterjee, An ensemble-based multiclass classifier for intrusion detection using Internet of Things, Computational Intelligence and Neuroscience 2022 (2022), Article ID 1668676, DOI: 10.1155/2022/1668676.
Samita, A review on intrusion detection systems for IoT-based systems, SN Computer Science 5 (2024), article number 380, DOI: 10.1007/s42979-024-02702-x.
M. Sarhan, S. Layeghy, N. Moustafa and M. Portmann, NetFlow datasets for machine learning based network intrusion detection systems, in: Big Data Technologies and Applications, Z. Deze, H. Huang, R. Hou, S. Rho and N. Chilamkurti (editors), (BDTA WiCON 2020), Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 371, Springer, Cham., pp. 117–135 (2021), DOI: 10.1007/978-3-030-72802-1_9.
S. A. Shirodkar, Brute force attacks detection on IoT networks using deep learning techniques, International Journal of Advanced Research in Science, Communication and Technology 3(3) (2023), 599 – 605, DOI: 10.48175/IJARSCT-11493.
R. Singh and R. L. Ujjwal, A comprehensive review of IoT-based IDS using intelligence technique, in: Advances in Data and Information Sciences (Lecture Notes in Networks and Systems, vol. 522), S. Tiwari, M. C. Trivedi, M. L. Kolhe and B. K. Singh (editors), Springer, Singapore (2023), DOI: 10.1007/978-981-19-5292-0_11.
P. Srikanth, An efficient approach for clustering and classification for fraud detection using bankruptcy data in IoT environment, International Journal of Information Technology 13(6) (2021), 2497 – 2503, DOI: 10.1007/s41870-021-00756-1.
R. S. Tiwari, D. Lakshmi, T. K. Das, A. K. Tripathy and K.-C. Li, A lightweight optimized intrusion detection system using machine learning for edge-based IIoT security, Telecommunication Systems 87 (2024), 605 – 624, DOI: 10.1007/s11235-024-01200-y.
S. Ullah, J. Ahmad, M. A. Khan, E. H. Alkhammash, M. Hadjouni, Y. Y. Ghadi, F. Saeed, and N. Pitropakis, A new intrusion detection system for the Internet of Things via deep convolutional neural network and feature engineering, Sensors 22(10) (2022), 3607, DOI: 10.3390/s22103607.
A. Vardhan, P. Kumar and L. K. Awasthi, A resilient intrusion detection system for IoT environment based on a modified stacking ensemble classifier, SN Computer Science 5 (2024), article number 1020, DOI: 10.1007/s42979-024-03364-5.
S. Yaras and M. Dener, IoT-based intrusion detection system using a new hybrid deep learning algorithm, Electronics 13(6) (2024), 1053, DOI: 10.3390/electronics13061053.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a CCAL that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
