Harnessing IoT Data: Machine Learning Approaches to Cybercrime Detection and Prevention
Abstract
Naveen Kumar Thawai and Sourabh Chaubey
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 investigates how IoT developments, whereas transformative, are progressively being misused by cybercriminals to conduct advanced assaults, compromising client protection, touchy information, and basic framework. By investigating current IoT vulnerabilities, real-world cyber episodes, and the advancing risk scene, we highlight how deficiently security measures in IoT gadgets make a ripe 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 vital requirement for multi-stakeholder collaboration—between governments, industry leaders, and security experts—to develop and implement comprehensive solutions that safeguard the imminent of IoT. This investigate gives bits of knowledge into the squeezing challenges postured by IoT-enabled cybercrime and offers suggestions for reinforcing IoT security.