Real-Time Explainable Malware Detection with Automated Response

Abstract: Modern computing systems are constantly exposed to unknown and evolving threats, making it difficult to ensure reliable protection using traditional security methods alone. While machine learning-based approaches have improved the ability to detect malicious activities, many of these systems still fail to clearly explain their decisions or respond quickly enough when a threat is identified. As a result, there is often a gap between detection, understanding, and action.

This paper presents a real-time malware detection framework that focuses on explainability, traceability, and automated response. The proposed system monitors system-level behavior and analyzes process activities using a transformer-based model that captures patterns over time. When a process is identified as suspicious, the system provides a clear, humanreadable explanation describing why it is considered malicious, along with traceable details such as where the activity originated and how it progressed within the system.

To minimize the impact of potential threats, the framework includes an automated response mechanism. If a process exceeds a defined risk threshold based on abnormal behavior, it is immediately terminated or isolated. In addition, a structured report is generated and stored, allowing users or analysts to review the complete details of the event whenever required.

Unlike existing approaches that treat detection and response separately, this work integrates detection, explanation, and action into a single unified framework. This not only reduces response time but also improves the clarity and usability of the system, making it more practical for real-world cybersecurity scenarios.

Furthermore, the system is designed to operate in real time without introducing significant overhead, ensuring that it remains efficient even in dynamic environments. By combining accurate detection with clear explanation and immediate response, the proposed approach aims to improve both trust and effectiveness in modern malware defense systems.

Keywords: Malware Detection, Explainable AI, Behavioral Analysis, Transformer Models, Real-Time Monitoring, Automated Response, Traceability, Cybersecurity