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.

翻译：物联网（IoT）的普及引发了人们对联网设备安全的担忧。为在攻击者利用漏洞之前加以应对，亟需开发合适且经济高效的方法来识别物联网设备中的漏洞。欺骗技术是提升物联网系统安全态势的重要方法之一，而蜜罐作为一种流行的欺骗技术，通过逼真地模拟交互行为，诱使未授权用户（攻击者）发动攻击。由于物联网设备数量庞大且具有异构性，手动构建低交互蜜罐和高交互蜜罐难以实现，这迫使研究者探索创新的物联网蜜罐构建方法。本文提出一种基于机器学习技术的物联网蜜罐，能够自动学习并与攻击者进行交互。对所提出模型的评估表明，我们的系统可延长与攻击者的会话时长，并捕获更多针对物联网网络的攻击。