An Adversarial Training Based Optimal Weighted Federated Deep Learning Framework for Malware Detection

ABSTRACT In recent years, the research area of cybersecurity has placed an emphasis on machine learning as an efficient security construct to be used in identifying the malignant malware within the contemporary anti‐malware programs. This study focuses on the crucial issue of privacy of data and ensuring that efficiency is achieved in detecting and classifying malware that is used. It is motivated by the fact that there is an increasing demand for privacy and secure machine learning methods in response to the growing threats to centralized data systems. The optimization‐based deep federated learning is an alternative approach to maintaining privacy without creating centralized points of data gathering. This paper, therefore, proposes a framework of system‐based malware detection through robust adversarial training that retains the identification with precise labeling. On adversarial samples, the suggested model applies projected gradient descent (PGD) and deep fool techniques when warping deep features with 11Multi‐Scale Dense Attention 1D dilated DCNN. The model employed in the accurate detection of malware classes is the optimal weighted federated deep learning (OWFDL). Local models and global models (gated recurrent neural networks (GRNN) with deep belief networks (DBN)) are used in this optimized weighted ensemble‐deep neural network (OWE‐DNN). In the proposed architecture, federated aggregation (FA) is used to accomplish model aggregation. The optimal parameter of the model is selected by chaotic brown bear optimization (CBBO). The implementation of this approach will be a major step forward in securing the industrial networks with important defensive measures to counter the current era of cyberattacks that threaten the fundamental systems of infrastructure. Additionally, Explainable artificial intelligence (XAI) is such that the human experts comprehend and interpret model detection, thereby raising the transparency and trust in the system. Finally, experiment evaluation used on two different datasets demonstrated the effectiveness of the proposed model, achieving 0.9884 accuracy on the SOMLAP dataset and 0.9985 on the Windows Malware dataset.