Deep learning enabled intrusion detection system for IoT security

Abstract

With the swift progress of technology and the increasing frequency of cyber attacks on organizational networks and systems, cybersecurity has become one of the most critical challenges in the modern digital world. Within this framework, Intrusion Detection Systems (IDS) are critical to mitigate the impact of cybercrimes and safeguarding systems from various malicious attacks. However, traditional machine learning (ML) approaches have ceased to be sufficient to handle the complexities of large-scale and unstructured data, particularly in Internet of Things (IoT) environments. To bridge this gap, we propose a hybrid DL-based IDS that leverages the synergistic strengths of convolutional neural networks (CNNs) and gated recurrent units (GRU). While CNNs excel at extracting spatial features from network traffic data, GRUs effectively model temporal dependencies, making their combination particularly well-suited for detecting dynamic and complex IoT intrusions. In this study, we evaluate three DL models including CNN, GRU, and a hybrid CNN-GRU approach on two recent Netflow-based datasets, NF-UNSW-NB15 and NF-CSE-CIC-IDS2018 for binary classification. Extensive experiments using accuracy, precision, recall, F1-score, False Alarm Rate (FAR), AUC, and processing time metrics show that the CNN-GRU model outperforms standalone CNN and GRU models, as well as existing literature approaches, achieving an accuracy of 98.60% on the NF-UNSW-NB15 dataset and 97.95% on the NF-CSE-CIC-IDS2018 dataset. Our findings underscore the efficacy of combining spatial and temporal DL architectures for robust IoT intrusion detection, offering a scalable solution for modern cyber threats.

Topics & Concepts