Explainable Attention-Based LSTM Framework for Early Detection of AI-Assisted Ransomware via File System Behavioral Analysis

2026-04-19

AI Summary

This study addresses the challenge of early detection of AI-assisted ransomware, which evades conventional security measures by mimicking legitimate system behavior. To overcome this limitation, the authors propose an attention-based Long Short-Term Memory (LSTM) model that performs temporal modeling of file system activity sequences, effectively capturing time-dependent patterns indicative of malicious operations. By integrating explainable artificial intelligence (XAI) techniques, the approach not only achieves high detection accuracy with low false-positive rates but also enables precise identification of threats at the earliest stages of an attack. Furthermore, the incorporation of XAI enhances the transparency and interpretability of the model’s decisions, offering a novel and robust framework for defending against highly sophisticated, AI-driven ransomware that closely emulates normal user behavior.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Ransomware continues to evolve as one of the most disruptive cyber threats, with recent variants increasingly leveraging automated and AI-assisted techniques to evade traditional signature-based defenses. Early detection of such attacks remains a significant challenge, particularly when malicious behavior closely resembles legitimate system activity. This study proposes an explainable attention-based Long Short-Term Memory (LSTM) framework for the early detection of AI assisted ransomware variants through analysis of file system behavioral patterns. The proposed model captures temporal dependencies in file operation sequences, while an attention mechanism highlights critical behavioral indicators associated with ransomware activity. To improve transparency and trust in automated detection systems, explainable artificial intelligence (XAI) techniques are incorporated to interpret model predictions and identify influential behavioral features. Experimental evaluation using ransomware behavioral traces demonstrates that the proposed framework can effectively distinguish malicious activity at early stages of execution with high detection performance and low false-positive rates. The findings suggest that combining sequence-aware deep learning models with explainability mechanisms can significantly enhance the reliability and interpretability of next-generation ransomware defense systems. This work contributes toward the development of intelligent and transparent cyber-defense mechanisms capable of addressing emerging AI-driven malware threats.

Problem

• AI-assisted ransomware
• early detection
• file system behavioral analysis
• explainable AI
• cyber defense

Innovation

• Explainable AI
• Attention Mechanism
• LSTM
• Ransomware Detection
• File System Behavior