Deep Learning Approaches for Enhanced Intrusion Detection in IoT Networks: A Comprehensive Overview
Year: 2026
Type: article
Abstract: The ubiquitous deployment of Internet of Things (IoT) infrastructures across critical domains has significantly escalated exposure to complex cyber threats. Traditional Intrusion Detection Systems (IDS) based on static signatures struggle to mitigate dynamic zero-day vectors within resource-constrained edge environments. To address these vulnerabilities, this paper presents a novel hybrid deep learning architecture that integrates Spatial Convolutional Neural Networks (CNN) with Temporal Long Short-Term Memory (LSTM) units. Utilizing the comprehensive EdgeIIoT-Dataset, our framework executes multi-stage data preprocessing, robust spatial feature extraction, and temporal sequence modeling to categorize both normal and malicious network traffic. Empirical evaluation demonstrates that the proposed CNN-LSTM model achieves an exceptional classification accuracy of 98.84% and an F1-score of 98.79%, vastly outperforming baseline architectures while maintaining minimal latency and low false-alarm ratios.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204227123
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