AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices

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 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.