Enhancing IoT Security through Machine Learning-Driven Anomaly Detection

Authors

DOI:

https://doi.org/10.21015/vtse.v12i1.1766

Abstract

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

lecturer

CS Department

Muhammad Fuzail, NFC Institute of Engineering and Technology,Multan

Lecturer

CS Department

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Published

2024-05-16

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

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Articles