Multimodal Multi‑Agent Framework Achieves High Accuracy in Ransomware Family Classification

Global: Multimodal Multi‑Agent Framework Achieves High Accuracy in Ransomware Family Classification

Researchers introduced a multimodal multi‑agent ransomware analysis framework that classifies ransomware families with high accuracy, evaluated on large‑scale datasets containing thousands of ransomware and benign samples. The system integrates static, dynamic, and network data through specialized agents and a fusion component, achieving a Macro‑F1 score of up to 0.936 and a composite quality score near 0.88 without fine‑tuning language models.

Ransomware Threat Landscape

Ransomware continues to be a leading cybersecurity threat, generating substantial financial losses and disrupting operations across multiple sectors worldwide. The rapid evolution of ransomware families and their use of sophisticated evasion techniques have intensified the need for more robust detection solutions.

Shortcomings of Conventional Detection Techniques

Traditional approaches—including static analysis, heuristic scanning, and behavioral monitoring—often struggle when applied in isolation, as they may miss novel or heavily obfuscated ransomware variants. These methods can produce high false‑positive rates or fail to capture the full spectrum of malicious activity.

Design of the Multimodal Multi‑Agent Architecture

The proposed system employs three dedicated agents, each responsible for processing a distinct data modality: static file attributes, dynamic execution traces, and network communication patterns. Each agent utilizes an auto‑encoder to extract compact feature representations, which are then passed to a central fusion agent. The fused representation feeds a transformer‑based classifier that predicts the specific ransomware family.

Iterative Inter‑Agent Feedback Loop

An inter‑agent feedback mechanism allows the agents to exchange confidence metrics, suppressing low‑confidence information and iteratively refining the feature embeddings. Over 100 training epochs, this feedback loop demonstrated stable monotonic convergence, delivering an absolute improvement of more than +0.75 in agent quality.

Experimental Evaluation and Results

Extensive experiments on a dataset comprising thousands of ransomware and benign samples showed that the multimodal approach outperformed single‑modality baselines and non‑adaptive fusion strategies. The framework achieved a Macro‑F1 score of 0.936 for family classification and reduced calibration error, while the overall composite score stabilized around 0.88 without any language‑model fine‑tuning.

Confidence‑Aware Abstention for Real‑World Deployment

To enhance reliability, the system incorporates a confidence‑aware abstention policy that favors conservative decisions when classification confidence falls below a predefined threshold. This strategy mitigates the risk of forced misclassifications and supports trustworthy deployment in operational environments.

Remaining Challenges and Future Directions

Despite strong performance, detection of zero‑day ransomware remains dependent on the ability to handle polymorphic code and modality disruptions. Ongoing research aims to improve adaptability to unseen families and to integrate additional contextual signals.

Implications for Cybersecurity Defenses

The findings suggest that a multimodal, feedback‑driven architecture can substantially strengthen ransomware detection pipelines, offering a practical path toward more resilient defensive systems.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.