An intelligent federated learning boosted cyberattack detection system for Denial-Of-Wallet attack using advanced...

In the modern digital era, owing to technological progressions, the diversification and intensity of cyber-attacks have attained an extraordinary level. Unlike network users, intruders use technological developments and implement attacks to cause operational disruptions, data breaches, and financial losses. The Denial-of-Wallet (DoW) attack adapts the standard Denial-of-Service (DoS) attack. The principle of either attack is equivalent: to use the feedback capability to flood requirements to a service, making it unable to utilize it correctly. The DoW attack goal is to use the limitation of the calculating capability dealing with the cloud service, trying to cause direct financial loss. Federated Learning (FL) has been developed as a guaranteed solution for detecting DoW. This model deals with safety concerns, minimizes the data breach risk, and improves scalability. This manuscript presents a Cyberattack Detection Model for Denial-Of-Wallet Using Advanced Metaheuristic Optimization Algorithms in Federated Learning (CDMDoW-AMOAFL) model. The proposed CDMDoW-AMOAFL model aims to detect and mitigate malicious activities in a network. The z-score normalization is initially applied in the data normalization stage to transform input data into a beneficial format. Furthermore, the proposed CDMDoW-AMOAFL method utilizes the Harris hawk optimization (HHO) model for the feature selection process to identify and select the most relevant features from a dataset. For cyberattack detection, the ensemble models, namely the gated recurrent unit (GRU), temporal convolutional network (TCN), and convolutional autoencoder (CAE) models, are employed. Finally, the modified marine predator algorithm (MMPA) optimally adjusts ensemble models' hyperparameter values, resulting in better classification performance. A wide-ranging experimentation was performed to prove the performance of the CDMDoW-AMOAFL method under the DoW attack detection dataset. The performance validation of the CDMDoW-AMOAFL technique illustrated a superior accuracy value of 98.12% over existing models.