ArticlePeerJ. Computer science2023
A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- AI-Driven Security: Detecting Cyber Attacks in IoT Networks.Sensors (Basel, Switzerland) · 2026Article
- LiRPE 1.0: a modified linformer with relational positional encoding for network intrusion detection.Scientific reports · 2026Article
- Sparse-selective quantization for real-time cyber threat detection in large-scale networks.PloS one · 2026Article
- Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in IoT networks.Scientific reports · 2025Article
- A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers.Sensors (Basel, Switzerland) · 2025Article
- KronNet a lightweight Kronecker enhanced feed forward neural network for efficient IoT intrusion detection.Scientific reports · 2025Article
- Smart intrusion detection model to identify unknown attacks for improved road safety and management.Scientific reports · 2025Article
- Securing IoT Networks Against DDoS Attacks: A Hybrid Deep Learning Approach.Sensors (Basel, Switzerland) · 2025Article
- Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment.Scientific reports · 2025Article
- Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks.Scientific reports · 2025Article
- An improved intrusion detection method for IIoT using attention mechanisms, BiGRU, and Inception-CNN.Scientific reports · 2024Article
- Anomaly detection in IoT-based healthcare: machine learning for enhanced security.Scientific reports · 2024Article
- NIDS-FGPA: A federated learning network intrusion detection algorithm based on secure aggregation of gradient similarity models.PloS one · 2024Article
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Authors and funding
6 authors.
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Abstract
Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.
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