โก Quick Summary
This study introduces an Optimised Multi-Head Self-Attention Model for enhancing IoT security through intelligent threat detection, achieving impressive accuracy rates of 99.11% and 99.18% on dual datasets. The research highlights the importance of privacy-preserving techniques in combating rising cybersecurity threats.
๐ Key Details
- ๐ Datasets: Edge-IIoT and ToN-IoT
- ๐งฉ Techniques used: Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), Multi-Head Self-Attention Mechanism (MHSAM)
- โ๏ธ Optimization methods: Crayfish Optimisation Algorithm (COA) and Plant Rhizome Growth Optimization (PRGO)
- ๐ Performance: Accuracy rates of 99.11% and 99.18%
๐ Key Takeaways
- ๐ Privacy-preserving techniques are crucial for protecting sensitive data in IoT networks.
- ๐ค Deep learning offers a promising alternative to traditional intrusion detection systems.
- ๐ฑ The OMHSA-IDPRGO model utilizes plant rhizome growth optimization for hyperparameter selection.
- ๐ The hybrid model combines CNN and BiGRU with a multi-head self-attention mechanism for enhanced detection.
- โก The study addresses the inefficiencies of conventional IDS in identifying zero-day attacks.
- ๐ Results indicate a significant advancement in IoT security measures.
- ๐ง The research emphasizes the need for automated cyberattack detection systems.
- ๐ Published in: Sci Rep, 2025.
๐ Background
The Internet of Things (IoT) has transformed how devices interact, offering unparalleled convenience and efficiency. However, this interconnectedness also brings significant security risks. As cyber threats evolve, the need for robust intrusion detection systems (IDSs) becomes increasingly critical. Traditional IDSs often struggle to keep pace with new attack vectors, necessitating innovative approaches to cybersecurity.
๐๏ธ Study
This research focuses on developing an automated cyberattack detection system tailored for IoT environments. The authors propose the OMHSA-IDPRGO method, which integrates advanced techniques to enhance the detection of cyber threats. The study employs a structured mean normalization process and utilizes the Crayfish Optimisation Algorithm for optimal feature selection, ensuring that the most relevant data is analyzed.
๐ Results
The OMHSA-IDPRGO model demonstrated remarkable performance, achieving accuracy rates of 99.11% and 99.18% on the Edge-IIoT and ToN-IoT datasets, respectively. These results underscore the model’s effectiveness in identifying and classifying cyber threats, particularly zero-day attacks that often evade traditional detection methods.
๐ Impact and Implications
The findings from this study have significant implications for the future of IoT security. By leveraging advanced deep learning techniques and privacy-preserving methods, the proposed model can enhance the resilience of IoT networks against cyber threats. This research paves the way for more secure and efficient IoT systems, ultimately contributing to safer digital environments for users and organizations alike.
๐ฎ Conclusion
This study highlights the transformative potential of integrating deep learning with privacy-preserving techniques in IoT security. The OMHSA-IDPRGO model represents a significant step forward in developing intelligent intrusion detection systems capable of addressing modern cybersecurity challenges. As the landscape of cyber threats continues to evolve, ongoing research in this area will be vital for ensuring the safety and integrity of IoT networks.
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Advances in IoT networks using privacy-preserving techniques with optimized multi-head self-attention model for intelligent threat detection based on plant rhizome growth optimization.
Abstract
The advances in the Internet of Things (IoT) involve a technology of interconnected devices that interact over the internet, providing convenience and efficiency while also posing significant security risks. Privacy-preserving techniques play a vital role in safeguarding sensitive user data while maintaining system efficiency. The rising tendency of cybersecurity threats and the need to recognize harmful activities in heterogeneous but resource-constrained settings have led to the development of sophisticated intrusion detection systems (IDSs) for quickly identifying intrusion efforts. Conventional IDSs are becoming more inefficient in classifying new attacks (zero-day attacks) whose designs are similar to any threat signatures. To reduce these restrictions, projected IDS depend on deep learning (DL). Due to DL techniques learning from vast amounts of data, they can identify novel, emerging attacks, making them an alternative method to classical cybersecurity. This study proposes an Optimised Multi-Head Self-Attention Model for an Intelligent Intrusion Detection Framework Using Plant Rhizome Growth Optimisation (OMHSA-IDPRGO) method to advance IoT security. The primary focus is on developing an automated cyberattack detection system for an IoT environment by employing advanced techniques. Initially, the mean normalization process is used to measure input data into a structured format. Furthermore, the Crayfish Optimisation Algorithm (COA) is used for optimal feature subset selection, identifying the most relevant features from the dataset. For the cybersecurity detection process, the OMHSA-IDPRGO method uses a hybrid model that encompasses a convolutional neural network and a bidirectional gated recurrent unit with a multi-head self-attention mechanism (CNN-BiGRU-MHSAM) technique. Finally, the hyperparameter selection is performed using the plant rhizome growth optimization (PRGO) approach to enhance classification performance. The experimentation of the OMHSA-IDPRGO model is examined under Edge-IIoT and ToN-IoT datasets. The comparison study of the OMHSA-IDPRGO model showed superior accuracy values of 99.11 and 99.18% compared to existing techniques on the dual datasets.
Author: [‘Alkhonaini MA’, ‘Ghorashi SA’, ‘Alshammri GH’, ‘Alshahrani S’, ‘Ebad SA’, ‘Albouq SS’, ‘Alzahrani F’, ‘Alshammeri M’]
Journal: Sci Rep
Citation: Alkhonaini MA, et al. Advances in IoT networks using privacy-preserving techniques with optimized multi-head self-attention model for intelligent threat detection based on plant rhizome growth optimization. Advances in IoT networks using privacy-preserving techniques with optimized multi-head self-attention model for intelligent threat detection based on plant rhizome growth optimization. 2025; 15:34233. doi: 10.1038/s41598-025-16052-y