Advancing Real-Time Malware Detection with Deep Learning for Proactive Threat Mitigation

Abstract

In the evolving landscape of cybersecurity, the rapid identification and mitigation of malware threats are critical to protecting sensitive systems and data. Traditional signature-based approaches to malware detection are increasingly ineffective against sophisticated, zero-day, and polymorphic threats. This paper explores the application of deep learning techniques in enhancing real-time malware detection, focusing on the ability of neural networks to recognize previously unseen patterns and behaviors indicative of malicious activity. We present a comprehensive framework that integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to improve accuracy and efficiency in detecting malware in dynamic environments. The proposed model leverages large-scale datasets to train deep learning algorithms capable of classifying both known and unknown threats with high precision. Through extensive testing and evaluation, the model demonstrates superior performance over conventional methods in terms of detection speed, false positive rates, and adaptability to evolving malware tactics. This research underscores the potential of deep learning in advancing proactive threat mitigation strategies, offering a scalable solution to combat the growing challenges in cybersecurity.