ArticleSensors (Basel, Switzerland)2023
Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Introducing a hybrid intrusion detection method for IoT-cloud environments based on ResNeXt and improved Ebola optimization search algorithm.Scientific reports · 2025Article
- Network intrusion detection model using wrapper based feature selection and multi head attention transformers.Scientific reports · 2025Article
- Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection.Scientific reports · 2024Article
- An improved intrusion detection method for IIoT using attention mechanisms, BiGRU, and Inception-CNN.Scientific reports · 2024Article
- Proactive Threat Hunting in Critical Infrastructure Protection through Hybrid Machine Learning Algorithm Application.Sensors (Basel, Switzerland) · 2024Article
- A Novel IDS with a Dynamic Access Control Algorithm to Detect and Defend Intrusion at IoT Nodes.Sensors (Basel, Switzerland) · 2024Article
- NIDS-FGPA: A federated learning network intrusion detection algorithm based on secure aggregation of gradient similarity models.PloS one · 2024Article
- An Enhanced Food Digestion Algorithm for Mobile Sensor Localization.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors.
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Abstract
Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.
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