IoTSecUT: Uncertainty-Based Hybrid Deep Learning Approach for Superior IoT Security Amidst Evolving Cyber Threats

Abstract

The rapid expansion of digital infrastructure has led to increased security threats. Deep Learning (DL) algorithms have emerged as potent tools for detecting cyberattacks in IoT ecosystems. However, challenges like imbalanced class distribution and vast data volumes remain, resulting in inaccurate classification outcomes and inflated accuracy rates. Additionally, memory-constrained IoT devices often find it challenging to accommodate robust DL methods. This paper introduces an innovative approach that addresses class imbalance and the challenges posed by high-dimensional data in intrusion detection systems. Our framework encompasses: (1) a conditional Generative Adversarial Network (cGAN) for minority class upsampling, (2) an auxiliary autoencoder for dimensionality reduction, and (3) an unique hybrid uncertainty-based transformer architecture for efficient network traffic classification. We undertake extensive experiments on two specific datasets: BoT-IoT and CICIDS2018, affirming the efficacy of our hybrid DL-based approach. Initially, we assess the quality of synthetic data produced by various techniques, comparing their Principal Component Analysis (PCA) plots to authentic data. Our GAN-generated data closely mirror the PCA of real data, denoting a high similarity in distribution. Subsequently, we benchmark our auxiliary autoencoder against established dimensionality reduction techniques. The results indicate that our auxiliary autoencoder has significantly lower noise levels than contemporary methods, reducing the data storage volume of network traffic by 93.02% on BoT-IoT and 96.25% on CICIDS2018 datasets. Lastly, we illustrate the enhanced capability of our uncertainty-based attention model in detecting cyberattacks across both datasets.

Topics & Concepts