Density Boosts Everything: A One-stop Strategy for Improving Performance, Robustness, and Sustainability of Malware Detectors

—In the contemporary landscape of cybersecurity, AI-driven detectors have emerged as pivotal in the realm of malware detection. However, existing AI-driven detectors encounter a myriad of challenges, including poisoning attacks, evasion attacks, and concept drift, which stem from the inherent characteristics of AI methodologies. While numerous solutions have been proposed to address these issues, they often concentrate on isolated problems, neglecting the broader implications for other facets of malware detection. This paper diverges from the conventional approach by not targeting a singular issue but instead identifying one of the fundamental causes of these challenges, sparsity. Sparsity refers to a scenario where certain feature values occur with low frequency, being represented only a minimal number of times across the dataset. The authors elevate the significance of sparsity and link it to core challenges in the domain of malware detection, and then aim to improve performance, robustness, and sustainability simultaneously by solving sparsity problems. To address the sparsity problems, a novel compression technique is designed to effectively alleviate the sparsity. Concurrently, a density boosting training method is proposed to consistently fill sparse regions. The proposed strategies are applied to PE, Android and PDF datasets, respectively. Empirical results demonstrate that the proposed methodologies not only successfully bolster the model’s