Risk Assessment in Cyber Attacks Based on Internet of Things (Iot)

The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and convenience. However, it has also exposed these devices to a growing number of cyber threats. This study explores the evolving landscape of IoT cyber-attacks and investigates the role of artificial intelligence (AI) in enhancing the security of IoT ecosystems. IoT devices, ranging from smart thermostats and wearables to industrial sensors and autonomous vehicles, have become integral parts of our daily lives and critical infrastructures. With their increasing prevalence, they have become attractive targets for cybercriminals. Traditional security mechanisms often fall short in detecting and mitigating these attacks, making AI a promising solution. This research leverages AI techniques such as machine learning, deep learning, and anomaly detection to analyze patterns and trends in IoT cyber-attacks. By examining a vast dataset of real-world incidents, including botnet-driven DDoS attacks, device compromise, and data breaches, the study identifies common attack vectors and vulnerabilities inherent to IoT devices. Furthermore, it assesses the effectiveness of AI-driven intrusion detection and prevention systems in real-time threat identification.