Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement learning with frilled lizard algorithm.

A denial of service (DoS) attack is an essential and nonstop threat to cybersecurity. Generally, DoS attacks are executed by forcing a victim's computer to reset and consume its sources. Distributed DoS (DDoS) is the most underlined and significant attack in today's cyber world. DDoS attacks have become a major threat on the Internet. Federated Learning (FL) is gaining attention in cybersecurity for collaboratively training deep learning (DL) models on distributed threat data without sharing raw data. However, the utilization of FL in this field is still in its early stages, with several key aspects yet to be explored. DL and machine learning (ML) enhance the ability to detect malicious traffic. Generally, DL and ML methods depend on necessary data samples for training a method using accuracy and efficacy. This manuscript proposes a Mitigating DDoS attack in Federated Learning Using Deep Reinforcement Learning and Frilled Lizard Optimization (MDDoSFL-DRLFLO) technique. The proposed MDDoSFL-DRLFLO model presents a collaborative FL approach to recognize and classify DDoS attacks quickly. At first, data normalization is performed using z-score normalization to standardize the input data within a specified range. Next, the feature selection process is implemented using an improved bacterial foraging optimization algorithm (IBFOA). In addition, the Dueling Double Deep Q-Network (D3QN) model is employed for the classification process. Finally, the hyperparameter tuning of the D3QN model is performed by using the frilled lizard optimization (FLO) approach. A wide range of experimental studies are implemented to ensure the enhanced performance of the MDDoSFL-DRLFLO method under the CICIDIS 2017 and ToN-IoT datasets. The performance validation of the MDDoSFL-DRLFLO technique portrayed a superior accuracy value of 99.52% on existing techniques under diverse evaluation measures.