A Novel Approach for RPL Based One and Multi-attacker Flood Attack Analysis

Abstract

The Internet of Things (IoT) encompasses a vast network of interconnected devices, vehicles, appliances, and other items with embedded electronics, software, sensors, and connectivity, allowing them to collect and exchange data. However, the growing number of connected devices raises concerns about IoT cybersecurity. Ensuring the security of sensitive information transmitted by IoT devices is crucial to prevent data breaches and cyberattacks. IoT cybersecurity involves employing various technologies, standards, and best practices, including encryption, firewalls, and multi-factor authentication. Although IoT offers numerous benefits, addressing its security challenges is essential. In this study, a flood attack, a significant threat to IoT devices, was executed to assess the system’s impact. A reference model without the attack was used to analyze network traffic involving single or multiple attackers. To prevent additional load on the operational system, network packets were mirrored via the cloud and transferred to artificial intelligence (AI) and forensic analysis tools in real-time. The study aimed to ensure continuity, a vital aspect of IoT system cybersecurity, by detecting the attacker using AI and analyzing real-time data with forensic analysis tools for continuous network monitoring. Various AI algorithms were evaluated for attacker detection, and the detection process proved successful.