ArticleComputational intelligence and neuroscience2022
Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- A novel adaptive Gaussian FOPID controller design for automatic voltage regulator system.Scientific reports · 2026Article
- A novel deep learning framework with temporal attention convolutional networks for intrusion detection in IoT and IIoT networks.Scientific reports · 2025Article
- Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems.Scientific reports · 2024Article
- Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering.Materials (Basel, Switzerland) · 2024Review
- OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems.Heliyon · 2024Article
- Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization.Biomimetics (Basel, Switzerland) · 2023Review
- Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Article
- Hybridized bio-inspired intrusion detection system for Internet of Things.Frontiers in big data · 2023Article
- Anomaly Detection in Industrial IoT Using Distributional Reinforcement Learning and Generative Adversarial Networks.Sensors (Basel, Switzerland) · 2022Article
- The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis.Biosensors · 2022Article
- Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach.Computational intelligence and neuroscience · 2022Article
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7 authors.
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Abstract
This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems.
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