Alanazi Adwan A, Althbiti Ashrf, Ghorashi Sara Abdelwahab, Birkea Fathea M O, Soto-Diaz Roosvel, Escorcia-Gutierrez José
Department of Computer Science and Information, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
Department of Information Technology, College of Computers and Information Technology, Taif University, 21944, Taif, Saudi Arabia.
Sci Rep. 2025 Sep 26;15(1):33291. doi: 10.1038/s41598-025-15052-2.
With the fast development of Internet of Things (IoT) devices, it is urgently needed to understand the real-time cybersecurity risks posed to them actively. In the ever-growing field of IoT environments, Distributed Denial of Service (DDoS) threats pose an essential challenge, cooperating with the reliability of these methods. These attacks are usually utilized in real-time to write down e-commerce platforms, government websites, and banking systems. To deal with the DDoS attacks, there's an increased interest in decentralized learning methods, especially federated learning (FL), a newly acquired enhanced examination from the cyberattack cooperatively trained deep learning (DL) methods with dispersed cyber threats summaries. The recommendation of FL resolves the data privacy problem successfully. FL intends to form a global approach by allowing multi-participants with local information to train a similar method in a distributed way, with outcomes without replacing sample data. This paper presents a Metaheuristic-Driven Dimensionality Reduction for Robust Attack Defense Using Deep Learning Models (MDRRAD-DLM) in real-world IoT applications. The aim is to propose effective detection and mitigation strategies for DDoS attacks. The data preprocessing phase initially applies Z-score normalization to transform the input data into a standardized format. Furthermore, the parrot optimization (PO) technique is employed for the feature selection process to select the significant and relevant features from input data. Moreover, the temporal convolutional network and bi-directional gated recurrent unit with multi-head attention (TCN-MHA-Bi-GRU) technique is implemented for the attack classification process. Finally, the elk herd optimizer (EHO) technique fine-tunes the parameter selection of the TCN-MHA-Bi-GRU technique. The efficiency of the MDRRAD-DLM approach is examined under NSLKDD and CIC-IDS2017 datasets. The experimental validation of the MDRRAD-DLM approach portrayed a superior accuracy value of 99.14% and 99.41% over the dual datasets.
随着物联网（IoT）设备的快速发展，迫切需要积极了解其面临的实时网络安全风险。在物联网环境不断发展的领域中，分布式拒绝服务（DDoS）威胁构成了一项重大挑战，这与这些方法的可靠性相关。这些攻击通常被实时用于瘫痪电子商务平台、政府网站和银行系统。为了应对DDoS攻击，人们对去中心化学习方法的兴趣日益增加，特别是联邦学习（FL），它是一种新出现的、经过改进的检测方法，通过协同训练的深度学习（DL）方法应对分散的网络威胁总结。FL的建议成功解决了数据隐私问题。FL旨在通过允许具有本地信息的多方参与者以分布式方式训练类似方法来形成一种全局方法，其结果无需替换样本数据。本文提出了一种用于现实世界物联网应用中基于深度学习模型的鲁棒攻击防御的元启发式驱动降维方法（MDRRAD-DLM）。其目的是提出有效的DDoS攻击检测和缓解策略。数据预处理阶段首先应用Z分数归一化将输入数据转换为标准化格式。此外，采用鹦鹉优化（PO）技术进行特征选择过程，从输入数据中选择重要且相关的特征。此外，将具有多头注意力的时间卷积网络和双向门控循环单元（TCN-MHA-Bi-GRU）技术用于攻击分类过程。最后，采用麋鹿群优化器（EHO）技术对TCN-MHA-Bi-GRU技术的参数选择进行微调。在NSLKDD和CIC-IDS2017数据集下检验了MDRRAD-DLM方法的效率。MDRRAD-DLM方法的实验验证表明，在这两个数据集上其准确率分别高达99.14%和99.41%。
