Title:
Investigating a Vulnerability Assessment on Security of Iot Application Layer Protocol: Using Agricultural Sector as Case Study

Authors:
Seun Mayowa Sunday, United Kingdom

Abstract:

This work proposes a machine-learning-based vulnerability identification and mitigating framework and explores the weaknesses in IoT application layer protocols, especially in the agriculture sector. By use of a systematic literature review (SLR), the study reveals shared hazards including man-in-the-middle attacks, Denial of Service (DoS) attacks, and Distributed Denial of Service (DDoS) attacks. The suggested system detects, categorizes, and responds to these hazards efficiently using adaptive machine learning models and real-time traffic analysis. By means of simulated attack scenarios, evaluation of the framework shows its capacity to greatly improve the security of IoT settings by providing real-time protection and flexibility to meet changing threats. With recommendations for additional developments to increase its applicability across different IoT contexts, this research helps the field by offering a specialised, IoT-oriented security framework that meets the special difficulties of securing IoT devices.

DOI: http://dx.doi.org/10.51505/ijaemr.2025.1522