IoT Device Identification and Cybersecurity: Advancements, Challenges, and an LSTM-MLP Solution

Abstract

Over the past few years, there has been an undeniable surge in the deployment of IoT devices. However, this rapid growth has brought new challenges in cybersecurity, as unauthorized device deployment, malicious code modification, malware deployment, and vulnerability exploitation have emerged as significant issues. As a result, there is a growing need for device identification mechanisms based on behavior monitoring. To address these challenges, Machine Learning (ML) and Deep Learning (DL) techniques have been increasingly employed due to advances in the field and improved processing capabilities. However, cyber attackers have developed adversarial attacks that focus on modifying contexts and evading ML evaluations applied to IoT device identification solutions. This article highlights the importance of addressing cybersecurity challenges in the IoT landscape and proposes a hardware behavior-based individual device identification approach using an LSTM-MLP architecture. The proposed architecture was compared to the most common ML/DL classification techniques using data collected from 45 Raspberry Pi devices running identical software and showing promising results in improving device identification. The proposed LSTM-MLP method outperformed previous solutions, achieving an average increase in F1-Score of +0.97 and a minimum TPR of 0.97 for all devices.