AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices
This addresses the need for cost-efficient vulnerability identification in IoT systems, though it appears incremental as it builds on existing deception techniques with machine learning enhancements.
The paper tackles the challenge of securing IoT devices by proposing an adaptive honeypot that uses machine learning to automatically interact with attackers, resulting in improved session length and capturing more attacks on IoT networks.
The proliferation of the Internet of Things (IoT) has raised concerns about the security of connected devices. There is a need to develop suitable and cost-efficient methods to identify vulnerabilities in IoT devices in order to address them before attackers seize opportunities to compromise them. The deception technique is a prominent approach to improving the security posture of IoT systems. Honeypot is a popular deception technique that mimics interaction in real fashion and encourages unauthorised users (attackers) to launch attacks. Due to the large number and the heterogeneity of IoT devices, manually crafting the low and high-interaction honeypots is not affordable. This has forced researchers to seek innovative ways to build honeypots for IoT devices. In this paper, we propose a honeypot for IoT devices that uses machine learning techniques to learn and interact with attackers automatically. The evaluation of the proposed model indicates that our system can improve the session length with attackers and capture more attacks on the IoT network.