AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices
Volviane Saphir Mfogo, Alain Zemkoho, Laurent Njilla, Marcellin, Nkenlifack, Charles Kamhoua

TL;DR
AIIPot is an adaptive honeypot system that leverages machine learning to automatically interact with attackers, enhancing IoT security by capturing more attacks and increasing attacker engagement.
Contribution
This paper introduces a novel machine learning-based adaptive honeypot for IoT devices, addressing the challenge of heterogeneity and automation in honeypot deployment.
Findings
Increased session length with attackers
Captured more attack instances
Improved detection of IoT vulnerabilities
Abstract
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…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
