Stratifying Malware Clusters: A Solution Mapping Paradigm

In the current cybersecurity landscape, a multitude of malware strains proliferate, often giving rise to a diverse array of variants. Malware clustering has become a common practice to classify these threats into distinct families and identify their similarities. However, existing approaches often conclude with clustering, overlooking the crucial step of mapping solutions to these clustered malware. Hence, this paper introduces a novel framework for mapping solutions to the outcomes of these clustering models. By doing so, it enables a more practical and efficient response to the ever-evolving threat landscape. When a new malware sample is classified, this framework allows for the exploration of solutions from closely related malware variants within the same cluster or sub-cluster. This approach empowers defenders to select the most fitting countermeasures by harnessing the insights provided by the clustering model and our proposed framework. In essence, this research aims to enhance the synergy between malware clustering models and practical cybersecurity defense. By coupling our framework with these clustering models, we facilitate a more informed and targeted response to emerging malware threats, ultimately bolstering our collective ability to safeguard against them.