Resampled Correlation-Based Feature Descriptors: A Novel Approach to Enhancing Malware Detection Capabilities

Abstract The study addresses the pressing need for improved malware detection in cybersecurity, leveraging a novel approach that combines deep learning with feature selection techniques. By analyzing network traffic patterns, the research aims to distinguish between benign and malicious activities, crucial for protecting networks and devices from cyber threats. Through training and evaluating Dense and LSTM neural network models on extensive malware datasets, the study demonstrates a significant enhancement in detection accuracy, surpassing previous methodologies by 0.98%. This innovative method not only provides valuable insights for developers but also contributes to advancing the capabilities of malware detection systems, ultimately bolstering cybersecurity defenses against evolving threats.