Qaddos Abdul, Yaseen Muhammad Usman, Al-Shamayleh Ahmad Sami, Imran Muhammad, Akhunzada Adnan, Alharthi Salman Z
Department of Computer Science, COMSATS University Islamabad (CUI), Islamabad, 45550, Pakistan.
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, 19328, Jordan.
Sci Rep. 2024 Sep 18;14(1):21789. doi: 10.1038/s41598-024-72049-z.
The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model's applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.
物联网（IoT）不断扩大的应用范围需要强大的入侵检测系统（IDS）来有效降低安全风险。然而，现有方法往往难以适应新出现的威胁，且未能考虑到物联网特有的复杂性。为应对这些挑战，本研究提出了一种新颖的方法，即将卷积神经网络（CNN）和门控循环单元（GRU）架构进行混合，专门用于物联网入侵检测。这种混合模型在捕捉复杂特征和学习物联网安全中至关重要的关系方面表现出色。此外，我们集成了特征加权合成少数过采样技术（FW-SMOTE）来处理通常困扰入侵检测任务的不平衡数据集。使用旨在模拟物联网环境的IoTID20数据集进行验证，在攻击检测中取得了99.60%的准确率这一优异结果，超过了现有基准。此外，在网络域数据集UNSW-NB15上的评估显示，其准确率为99.16%，性能稳健，突出了该模型在不同数据集上的适用性。这种创新方法不仅解决了物联网入侵检测中的当前局限性，还在准确性和适应性方面建立了新的基准。研究结果强调了其作为一种通用且有效的解决方案的潜力，可保护物联网生态系统免受不断演变的安全威胁。
