A lightweight intrusion detection system using deep convolutional neural network

Abstract

Intrusion Detection Systems (IDSs) serve as critical components of cybersecurity infrastructure, safeguarding computer networks against evolving cyber threats. Recent advancements in deep learning architectures, particularly Convolutional Neural Networks (CNNs), have demonstrated substantial potential in augmenting IDS efficacy. However, conventional CNN architectures exhibit inherent limitations in processing sequential data due to their inability to capture long-term temporal dependencies. To address these operational constraints, this study proposes a lightweight deep convolutional neural network-based intrusion detection system (LWIDS-DCNN), a novel framework designed to optimize feature extraction and detection accuracy in heterogeneous network environments. The LWIDS-DCNN architecture strategically integrates convolutional layers with pooling operations and fully connected layers, forming an optimized algorithmic structure tailored for efficient extraction of discriminative features from network traffic data. The framework incorporates adaptive accelerator algorithms and dynamic learning rate optimization strategies to ensure accelerated convergence rates while maintaining training stability. Empirical validation was conducted using three benchmark datasets: CICIDS2017, CICIoMT2024, and InSDN. The proposed model achieved state-of-the-art detection accuracy, with results exceeding 99.93% on CICIDS2017, 99.70% on CICIoMT2024, and 99.97% on InSDN. A comprehensive comparative analysis against existing methodologies demonstrated LWIDS-DCNN’s superiority across key performance metrics, including precision, recall, F1-score, and loss rate. Notably, the system’s lightweight design ensures computational efficiency without compromising detection robustness, making it particularly suitable for resource-constrained environments. This work contributes to the advancement of network security research by introducing a scalable, high-performance IDS architecture capable of addressing the unique challenges posed by traditional networks, IoMT ecosystems, and SDN infrastructures. The LWIDS-DCNN framework establishes a foundational paradigm for real-time intrusion detection in converged environments, offering a robust, lightweight solution that addresses the unique intrusion detection requirements of emerging IoT and SDN systems.