Secure federated learning with metaheuristic optimized dimensionality reduction and multi-head attention for DDoS attack mitigation

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.