The ML-Based Sandbox Malware Visualizer

Abstract

The rapid evolution of malware, including polymorphic and zero-day threats, has rendered traditional static and dynamic detection systems increasingly ineffective [10]. Modern attackers employ anti-analysis and evasion strategies, making malware detection a significant challenge in cyber security [8]. To address this, we provide an ML-Based Sandbox Malware Visualizer, a dynamic analytic system that uses sandboxing, machine learning, and graphical visualization to improve malware detection [3] The system classifies malware samples, identifies new threats, and calculates confidence levels using machine learning techniques such as Random Forest, XGBoost, and Graph Neural Networks (GNNs) [1].

The program offers a graph-based representation that tracks attack vectors from entry sites to system exploitation and network activity to aid humans in comprehending the malware's operations [6]. Security analysts, researchers, and SOC teams may make judgments more quickly thanks to this visual representation of complex analysis [4]. To deliver actionable insight, the system also generates downloadable PDF/HTML reports that include network traces, severity scores, and Indicators of Compromise (IOCs)[3]. The ML-Based Sandbox Malware Visualizer is a vital tool for cyber security operations, research, and military organizations since it improves malware analysis transparency in addition to detection accuracy [6].

Future enhancements will include enhanced malware detection, both fileless and memory-resident [10] as well as integration with threat intelligence APIs [5].

KEYWORDS: Malware Detection, Sandbox Analysis, Machine Learning, Graph Neural Networks, Dynamic Malware Analysis.