A Transformer-Based Approach for DDoS Attack Detection in IoT Networks

TL;DR

This paper introduces a Transformer-based model for detecting DDoS attacks in IoT networks, leveraging self-attention to improve detection accuracy amid diverse protocols and high traffic volumes.

Contribution

It presents a novel application of Transformer models for IoT security, addressing the limitations of traditional detection methods in dynamic, resource-constrained environments.

Findings

Transformer model outperforms traditional machine learning techniques in detection accuracy

The approach achieves higher precision, recall, and F1-score on real-world datasets

Demonstrates potential for deployment in real-world IoT security systems

Abstract

DDoS attacks have become a major threat to the security of IoT devices and can cause severe damage to the network infrastructure. IoT devices suffer from the inherent problem of resource constraints and are therefore susceptible to such resource-exhausting attacks. Traditional methods for detecting DDoS attacks are not efficient enough to cope with the dynamic nature of IoT networks, as well as the scalability of the attacks, diversity of protocols, high volume of traffic, and variability in device behavior, and variability of protocols like MQTT, CoAP, making it hard to implement security across all the protocols. In this paper, we propose a novel approach, i.e., the use of Transformer models, which have shown remarkable performance in natural language processing tasks, for detecting DDoS attacks on IoT devices. The proposed model extracts features from network traffic data and processes them using a self-attention mechanism. Experiments conducted on a real-world dataset demonstrate that the proposed approach outperforms traditional machine learning techniques, which can be validated by comparing both approaches' accuracy, precision, recall, and F1-score. The results of this study show that the Transformer models can be an effective solution for detecting DDoS attacks on IoT devices and have the potential to be deployed in real-world IoT environments.