arXiv subjects

Asmitha K. A.

Publications and source records attributed to Asmitha K. A..

4 recordsLinked to original sources

arXiv2026

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families. Traditional single-model detection mechanisms often fail to generalize across such diverse data, leading to degraded performance, particularly on obfuscated and rare malware samples. In this work, we propose a unified multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The proposed system evaluates performance across two different input representations, i.e., high-dimensional EMBER feature sets and raw 1D byte arrays extracted from Portable Executable files. It simultaneously performs three critical tasks: malware family classification, packed versus unpacked detection, and malware versus benign identification. By decomposing the problem into specialized expert networks and employing adaptive gating mechanisms, the model enables effective task-specific learning while maintaining overall scalability. We investigate multiple architectural variants, including Homogeneous MoE, Heterogeneous MoE, and Multi-Gate MoE (MMoE). Performance is evaluated in both standard and adversarial settings using original and mutated samples. The obtained results demonstrate that the Multi-Gate MoE model achieves the best performance, reaching a combined detection rate of 0.9744 with only $2.56\%$ failure rate. Moreover, this configuration exhibits improved robustness under mutation-induced distribution shifts. Our findings highlight the effectiveness of expert specialization and task-specific routing in handling complex malware distributions, making the proposed framework a promising direction for scalable and resilient malware detection systems.

cs.CR↗

arXiv2026

Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks

Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address this vulnerability, we propose a certifiably robust malware detection framework based on randomized smoothing through feature ablation and targeted noise injection. During evaluation, our system analyzes an executable by generating multiple ablated variants, classifies them by using a smoothed classifier, and identifies the final label based on the majority vote. By analyzing the top-class voting distribution and the Wilson score interval, we derive a formal certificate that guarantees robustness within a specific radius against feature-space perturbations. We evaluate our approach by comparing the performance of the base classifier and the smoothed classifier on both clean executables and ablated variants generated using PyMetaEngine. Our results demonstrate that the proposed smoothed classifier successfully provides certifiable robustness against metamorphic evasion attacks without requiring modifications to the underlying machine learning architecture.

cs.CR↗

arXiv2025

Security through the Eyes of AI: How Visualization is Shaping Malware Detection

Malware, a persistent cybersecurity threat, increasingly targets interconnected digital systems such as desktop, mobile, and IoT platforms through sophisticated attack vectors. By exploiting these vulnerabilities, attackers compromise the integrity and resilience of modern digital ecosystems. To address this risk, security experts actively employ Machine Learning or Deep Learning-based strategies, integrating static, dynamic, or hybrid approaches to categorize malware instances. Despite their advantages, these methods have inherent drawbacks and malware variants persistently evolve with increased sophistication, necessitating advancements in detection strategies. Visualization-based techniques are emerging as scalable and interpretable solutions for detecting and understanding malicious behaviors across diverse platforms including desktop, mobile, IoT, and distributed systems as well as through analysis of network packet capture files. In this comprehensive survey of more than 100 high-quality research articles, we evaluate existing visualization-based approaches applied to malware detection and classification. As a first contribution, we propose a new all-encompassing framework to study the landscape of visualization-based malware detection techniques. Within this framework, we systematically analyze state-of-the-art approaches across the critical stages of the malware detection pipeline. By analyzing not only the single techniques but also how they are combined to produce the final solution, we shed light on the main challenges in visualization-based approaches and provide insights into the advancements and potential future directions in this critical field.

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arXiv2024

Deep Learning Fusion For Effective Malware Detection: Leveraging Visual Features

Malware has become a formidable threat as it has been growing exponentially in number and sophistication, thus, it is imperative to have a solution that is easy to implement, reliable, and effective. While recent research has introduced deep learning multi-feature fusion algorithms, they lack a proper explanation. In this work, we investigate the power of fusing Convolutional Neural Network models trained on different modalities of a malware executable. We are proposing a novel multimodal fusion algorithm, leveraging three different visual malware features: Grayscale Image, Entropy Graph, and SimHash Image, with which we conducted exhaustive experiments independently on each feature and combinations of all three of them using fusion operators such as average, maximum, add, and concatenate for effective malware detection and classification. The proposed strategy has a detection rate of 1.00 (on a scale of 0-1) in identifying malware in the given dataset. We explained its interpretability with visualization techniques such as t-SNE and Grad-CAM. Experimental results show the model works even for a highly imbalanced dataset. We also assessed the effectiveness of the proposed method on obfuscated malware and achieved state-of-the-art results. The proposed methodology is more reliable as our findings prove VGG16 model can detect and classify malware in a matter of seconds in real-time.

cs.CR↗