Identify spoofing attacks in Internet of Things (IoT) environments using machine learning algorithms

With the growing adoption of Internet of Things (IoT) devices, security concerns are becoming increasingly urgent. Protecting IoT systems from cyberattacks is crucial to safeguard sensitive information. Spoofing, particularly Domain Name System (DNS) and Address Resolution Protocol (ARP) spoofing, is a type of attack that can manipulate network traffic and compromise data integrity. DNS spoofing redirects users to fraudulent websites by altering domain name resolutions, while ARP spoofing tricks the network by associating a legitimate internet protocol address with a malicious MAC address, allowing attackers to intercept or modify communication. This study aims to develop an efficient method for detecting these types of spoofing attacks in IoT environments using machine learning techniques. The results show that the random forest algorithm outperforms other models, achieving remarkable performance with a 95.1% accuracy, a precision score of 95.2%, and a strong F1 score of 95.1%. A key contribution of this research is the simultaneous detection of both DNS and ARP spoofing within a unified framework, utilizing a comprehensive set of 46 features. These findings underscore the importance of ensuring robust protection against spoofing attacks to maintain the security and integrity of IoT systems.