Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

• Published in: IEEE access 2024, Vol.12, p.8337-8345
• Main Authors: Maghrabi, Louai A., Shabanah, Sahar, Althaqafi, Turki, Alsalman, Dheyaaldin, Algarni, Sultan, Al-Ghamdi, Abdullah Al-Malaise, Ragab, Mahmoud
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; botnet attacks; Cybersecurity; Deep learning; Feature extraction; Internet of Things; Machine learning; Malware; Optimization; parameter tuning; smart environment; Spatial data
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3352568

Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model’s capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures.