Enhancing Real-Time Malware Analysis with Quantum Neural Networks

Abstract

The proposed Quantum Neural Networks (QNN) perform better than traditional machine learning models. The escalating complexity of malware poses a significant challenge to cybersecurity, necessitating innovative approaches to keep pace with its rapid evolution. Contemporary malware analysis techniques underscore the urgent need for solutions that can adapt to the dynamic functionalities of evolving malware. In this context, Quantum Neural Networks (QNNs) emerge as a cutting-edge and distinctive approach to malware analysis, promising to overcome the limitations of conventional methods. Our exploration of QNNs focuses on uncovering their valuable applications, particularly in real-time malware research. We meticulously examine the advantages of QNNs in contrast to conventional machine-learning methods employed in malware detection and classification. The proposed QNN showcases its unique capability to handle complex patterns, emphasizing its potential to achieve heightened levels of accuracy. Our contribution extends to introducing a dedicated framework for QNN-based malware analysis, harnessing the formidable computational capabilities of quantum computing for real-time malware analysis. This framework is structured around three pivotal components, Malware Feature Extraction utilizes quantum feature extraction techniques to identify relevant features from malware samples. Malware Classification employs a QNN classifier to categorize malware samples as benign or malicious. Real-Time Analysis enables the instantaneous examination of malware samples by integrating feature extraction and classification within a streaming data pipeline. Our proposed methodology undergoes comprehensive evaluation using a benchmark dataset of malware samples. The Proposed Quantum Neural Networks (QNNs) demonstrated a high accuracy of 0.95, outperforming other quantum models such as Quantum Support Vector Machines (QSVM) and Quantum Decision Trees (QDT), as well as classical models like Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT) on the Malware DB dataset. The results affirm the framework's exceptional accuracy rates and low latency, establishing its suitability for real-time malware analysis. These findings underscore the potential for QNNs to revolutionize malware evaluation and strengthen real-time defenses against cyberattacks. While our research demonstrates promising outcomes, further exploration and development in this domain are imperative to fully exploit the extensive viability that QNNs offer for cybersecurity applications.