Advancing device and network security for enhanced privacy

Abstract

The rapid proliferation of IoT (Internet of Things) devices has ushered in an era of unprecedented connectivity and automation. The widespread adoption has also exposed vulnerabilities, necessitating robust security and privacy measures. This research presents a comprehensive study focused on enhancing IoT device and network security and privacy through empirical investigation and advanced machine learning techniques. Commencing with an exhaustive literature review, it was assessed, the evolving landscape of IoT security threats, solutions, and identified research gaps. Building upon the foundation, it was designed and rigorously evaluated a machine learning-based classification model tailored for IoT device security. Utilizing a meticulously crafted simulated dataset mirroring real-world IoT features, our model undergoes comprehensive performance evaluations. Metrics include accuracy, precision, recall, F1 score, and ROC analysis. Our findings reveal a nuanced performance profile, shedding light on the model's capability to accurately classify IoT devices as Secure or Vulnerable. Precision-recall trade-offs, emphasizing the need for a judicious balance to mitigate false positives and false negatives was investigated. The critical role of feature engineering and model refinement, pointing to areas for future research and optimization. This research contributes to the burgeoning field of IoT security by employing machine learning as a proactive tool for fortifying IoT device and network security. Our findings advocate for a strategic approach to secure IoT ecosystems, ensuring data integrity and privacy in the face of evolving threats. As IoT devices continue to proliferate across industries, this research serves as a foundation for innovative strategies and ongoing investigations to harness the full potential of secure IoT environments while addressing multifaceted challenges in the ever-evolving IoT landscape.