NEURAL NETWORK-BASED MALWARE ANALYSIS: A ROLE-BASED STUDY FOR MALWARE ANALYSTS IN CYBER DEFENSE

Abstract : This paper presents an in-depth study on the application of neural network-based approaches to malware analysis, specifically focusing on the role-based perspectives of malware analysts within cyber defense operations. With the increasing sophistication and volume of malware threats, traditional signature-based detection methods have become insufficient to keep pace with rapidly evolving malware variants. Neural networks, as a subset of deep learning, offer promising capabilities in automatic feature extraction and classification, enabling more accurate and timely detection of both known and novel malware. This research explores the design, implementation, and evaluation of various neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, tailored for static and dynamic malware analysis tasks. By leveraging large-scale datasets consisting of diverse malware samples and benign software, the study investigates the effectiveness of neural networks in identifying malware families, detecting polymorphic and metamorphic variants, and uncovering previously unseen threats. Beyond the technical evaluation, the study adopts a role-based analytical framework to assess how different categories of malware analysts—ranging from junior analysts to threat hunters and incident responders—interact with and benefit from neural network-driven tools. It highlights how neural network-based analysis can enhance the cognitive workflows of analysts by automating routine classification tasks, prioritizing suspicious samples, and generating interpretable insights to support decision-making. The paper also addresses the challenges faced by analysts in integrating AI-driven tools into existing cybersecurity infrastructures, including issues related to model explainability, false positives, and the need for continuous model retraining to adapt to emerging threats. To bridge the gap between advanced neural techniques and practical malware analysis, the research proposes a user-centric interface design and adaptive feedback mechanisms that allow analysts to refine model outputs based on domain expertise, thereby improving detection accuracy and reducing alert fatigue. Experimental results demonstrate that neural network-based malware analysis achieves high accuracy and robustness across various malware categories, significantly outperforming traditional machine learning baselines. The role-based study further reveals that analysts’ trust and reliance on these AI tools are strongly influenced by the transparency and usability of the systems. Ultimately, this work contributes to advancing cyber defense capabilities by offering a comprehensive understanding of how neural network models can be effectively deployed to augment human analysts in combating increasingly complex malware threats, fostering a synergistic collaboration between automated intelligence and human expertise in modern security operations.

Keyword Neural Networks, Malware Analysis, Cyber Defense, Deep Learning, Role-Based Study, Threat Detection