Adapting to Evolving Threats: A Comprehensive Review of Virus Total’s Performance Versus Cloud-Native Malware Detection Solutions

Abstract

The rapid expansion of cloud computing environments introduces significant challenges to data security, particularly in the area of malware detection. VirusTotal (VT), a widely used cloud-based malware detection tool, has become a standard for file and URL analysis, and it works by aggregating results from multiple antivirus engines. However, as the sophistication of malware continues to evolve, there is increasing concern about VT’s effectiveness in identifying advanced threats in dynamic cloud environments. This review systematically evaluates the capabilities of VT, benchmarks its performance against other cloud-based malware detection solutions, and highlights its strengths and limitations. This study focuses on two critical metrics, detection rates and false positive outcomes, which directly impact the balance between security accuracy and operational efficiency in cloud infrastructures. This review also addresses the challenges VT faces in detecting polymorphic, metamorphic, and evasive malware, which often evade traditional signature-based detection systems. While VT excels in quickly identifying known malware, it struggles with stealthy and sophisticated threats due to its reliance on signature-based methods and lack of contextual threat insights. Additionally, VT’s scalability issues in large-scale enterprise environments further limit its effectiveness as a comprehensive detection solution. This study underscores the need for advanced, AI-driven, and behavior-based analysis techniques in cloud-native malware detection systems and proposes potential hybrid solutions that integrate VT’s multi-engine aggregation with machine learning models to address these emerging challenges.