Enhancing malware detection with genetic algorithms and generative adversarial networks

Abstract

Malware detection is a critical task in cybersecurity, necessitating the creation of robust and accurate detection models. Our proposal employs a holistic methodology for identifying and mitigating malware using deep learning techniques. Initially, a customized genetic algorithm is employed for feature selection, reducing dimensionality and enhancing the discriminatory power of the dataset. Subsequently, a deep neural network is trained on the selected features, achieving high accuracy and robust performance in distinguishing between malware and benign data. Generative adversarial networks are also utilized to evaluate model effectiveness on unseen data and ensure the model's robustness and generalization capabilities. Evaluation of the proposed model demonstrates accurate malware detection with high generalization capabilities. Furthermore, future research should focus on developing and deploying practical tools or systems that implement the proposed model for real-time malware detection in operational environments. This research makes a significant contribution to the field of malware detection and provides excellent opportunities for practical implementation in the field of cybersecurity.