Harnessing IoT Data: Machine Learning Approaches to Cybercrime Detection and Prevention
Naveen Kumar Thawait · Preprints.org · 2024
Abstract: The rapid adoption of the Internet of Things (IoT) has revolutionized industries, enhancing connectivity, automation, and efficiency across sectors such as healthcare, transportation, and smart homes. However, this technological advancement comes with significant cybersecurity challenges. This paper explores how IoT innovations, while transformative, are increasingly being exploited by cybercriminals to conduct sophisticated attacks, compromising user privacy, sensitive data, and critical infrastructure. By reviewing current IoT vulnerabilities, real-world cyber incidents, and the evolving threat landscape, we highlight how insufficient security measures in IoT devices create a fertile ground for cybercrime. The study employs a combination of case studies and data analysis to examine key vulnerabilities, including weak authentication, poor encryption, and insecure communication protocols. Additionally, the paper discusses how advanced technologies, such as artificial intelligence (AI) and machine learning (ML), are being utilized by cybercriminals to exploit these weaknesses at scale. The findings reveal that the current regulatory frameworks are insufficient to address the growing cyber risks associated with IoT, underscoring the need for robust security policies, industry standards, and proactive threat mitigation strategies. In conclusion, the paper emphasizes the urgent need for multi-stakeholder collaboration—between governments, industry leaders, and security experts—to develop and implement comprehensive solutions that safeguard the future of IoT. This research provides insights into the pressing challenges posed by IoT-enabled cybercrime and offers recommendations for strengthening IoT security.