Enhancing IoT Security through Machine Learning-Driven Anomaly Detection





This is study emphasizes the growing cybersecurity situations arising from the increasing use of Internet of Things (IoT) devices. Paying the main attention to the development of IoT security, the work here deploys the machine learning-based anomaly detection and adaptive defense mechanisms as proactive methods to counteract existing plus future cyber threat sources. The visual serves to expound the rapid development of the Internet of Things, and it also highlights the importance of infrastructures with robust safety features to secure the connected devices. IoT security statement brings out the hidden threat and vulnerabilities of the IoT, in this context advanced security measures are for the rescue. The objectives concentrate on improving security of IoT via machine learning detection of anomalies, and bring introduction of defense mechanisms that are adaptive.
We specify the data sources, preprocessing tasks, and Random Forest, Decision Tree, SVM, and Gradient Boosting algorithms selected for anomaly detection in the methodology section. The abnormity negotiation function and the self-adaptive defense procedures are combined in order to strengthen the information technology ecosystems which are capable of dynamic simplification. The results and discussion part hotelates the effectiveness of machine learning models selected, and indicates about accuracy, precision, and recall metrics. To state in the most significant matter, Gradient Boosting brings the greater precision of 89.34%. Table 3 below indicates the various models' effectiveness. It is proven that Gradient Boosting is the most powerful model among all. The discourse unfolds with account of the results, acknowledgment of the limitations, and discussion crucial obstacles encountered in the realization of the research. The conclusion reaffirms the importance of machine learning in IoT security implementation, thus building a robust system that can evolve to fight the ever-emerging cyber-attacks, keeping up with the progressive direction for securing IoT through the connected world.

Author Biographies

Muhammad Kamran Abid, NFC Institute of Engineering and Technology,Multan,Pakistan


CS Department

Muhammad Fuzail, NFC Institute of Engineering and Technology,Multan


CS Department


V. V. Raje, S. Goel, S. V. Patil, M. D. Kokate, D. A. Mane, and S. Lavate, “Realtime anomaly detection in healthcare IoT: A machine learning-driven security framework,” *Journal of Electrical Systems*, vol. 19, no. 3, pp. 1–8, 2023.

S. Akbar, K. T. Ahmad, M. K. Abid, and N. Aslam, “Wheat disease detection for yield management using IoT and deep learning techniques,” *VFAST Transactions on Software Engineering*, vol. 10, no. 3, pp. 80–89, 2022.

H. Bangui and B. Buhnova, “Recent advances in machine-learning driven intrusion detection in transportation: Survey,” *Procedia Computer Science*, vol. 184, pp. 877–886, 2021.

M. Ramzan, Z. U. R. Zia, M. K. Abid, N. Aslam, and M. Fuzail, “A review study on smart homes present challenges concerning awareness of security mechanism for Internet of Things (IoT),” *Journal of Computing & Biomedical Informatics*, vol. 2024, no. 1, pp. 1–10, 2024.

M. K. Abid, Z. U. R. Zia, and S. Farid, “Security and privacy for future healthcare IoT,” *Journal of Computing & Biomedical Informatics*, vol. 4, no. 1, pp. 132–140, 2022.

M. Nankya, R. Chataut, and R. Akl, “Securing industrial control systems: Components, cyber threats, and machine learning-driven defense strategies,” *Sensors*, vol. 23, no. 21, p. 8840, 2023.

M. K. Abid, M. Qadir, S. Farid, and M. Alam, “IoT environment security and privacy for smart homes,” *Journal of Information Communication Technologies and Robotic Applications*, vol. 13, no. 1, pp. 15–22, 2022.

D. Javeed, T. Gao, M. T. Khan, and I. Ahmad, “A hybrid deep learning-driven SDN enabled mechanism for secure communication in Internet of Things (IoT),” *Sensors*, vol. 21, no. 14, p. 4884, 2021.

M. A. Alsoufi, S. Razak, M. M. Siraj, I. Nafea, F. A. Ghaleb, F. Saeed, and M. Nasser, “Anomaly-based intrusion detection systems in IoT using deep learning: A systematic literature review,” *Applied sciences*, vol. 11, no. 18, p. 8383, 2021.

S. Bharati and P. Podder, “Machine and deep learning for IoT security and privacy: Applications, challenges, and future directions,” *Security and communication networks*, vol. 2022, pp. 1–41, 2022.

E. Gyamfi and A. Jurcut, “Intrusion detection in Internet of Things systems: A review on design approaches leveraging multi-access edge computing, machine learning, and datasets,” *Sensors*, vol. 22, no. 10, p. 3744, 2022.

M. Aslam, D. Ye, A. Tariq, M. Asad, M. Hanif, D. Ndzi, S. A. Chelloug, M. A. Elaziz, M. A. Al-Qaness, and S. F. Jilani, “Adaptive machine learning based distributed denial-of-services attacks detection and mitigation system for SDN-enabled IoT,” *Sensors*, vol. 22, no. 7, p. 2697, 2022.

S. K. Devineni, S. Kathiriya, and A. Shende, “Machine learning-powered anomaly detection: Enhancing data security and integrity,” *Journal of Artificial Intelligence & Cloud Computing*, vol. 184, pp. 2–9, 2023.

I. Ullah, A. Ullah, and M. Sajjad, “Towards a hybrid deep learning model for anomalous activities detection in Internet of Things networks,” *IoT*, vol. 2, no. 3, pp. 428–448, 2021.

Y. N. Kunang, S. Nurmaini, D. Stiawan, and B. Y. Suprapto, “Attack classification of an intrusion detection system using deep learning and hyperparameter optimization,” *Journal of Information Security and Applications*, vol. 58, p. 102804, 2021.

B. Hussain, Q. Du, B. Sun, and Z. Han, “Deep learning-based DDoS-attack detection for cyber–physical system over 5G network,” *IEEE Transactions on Industrial Informatics*, vol. 17, no. 2, pp. 860–870, 2020.

A. Haider, M. Adnan Khan, A. Rehman, M. Rahman, and H. Seok Kim, “A real-time sequential deep extreme learning machine cybersecurity intrusion detection system,” *Computers, Materials & Continua*, vol. 66, no. 2, pp. 1785–1798, 2021.

M. Catillo, M. Rak, and U. Villano, “2L-ZED-IDS: A two-level anomaly detector for multiple attack classes,” in *Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications (WAINA-2020)*, pp. 687–696, Springer, 2020.




How to Cite

Usama Tahir, Muhammad Kamran Abid, Muhammad Fuzail, & Naeem Aslam. (2024). Enhancing IoT Security through Machine Learning-Driven Anomaly Detection . VFAST Transactions on Software Engineering, 12(2), 01–13. https://doi.org/10.21015/vtse.v12i1.1766