Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models

TL;DR

This paper presents a hybrid deep learning approach combining SOMs, DBNs, and Autoencoders, optimized with PSO, to detect cyber-attacks on IoT networks with high accuracy and adaptability.

Contribution

It introduces a novel hybrid model for IoT attack detection that effectively identifies known and unknown threats, optimized through Particle Swarm Optimization.

Findings

Achieves up to 99.99% accuracy in attack detection

MCC values exceed 99.50%, indicating strong correlation

Performs well across diverse datasets like NSL-KDD, UNSW-NB15, CICIoT2023

Abstract

The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks, making effective detection essential for securing IoT networks. This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unseen attack patterns. A comprehensive evaluation using simulated and real-world traffic data is conducted, with models optimized via Particle Swarm Optimization (PSO). The system achieves an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments on NSL-KDD, UNSW-NB15, and CICIoT2023 confirm the model's strong performance across diverse attack types. These findings suggest that the proposed method enhances IoT security by identifying emerging threats and adapting to evolving attack strategies.