TL-RL-FusionNet is a novel reinforcement learning-guided framework that enhances ransomware detection by adaptively focusing on complex, evolving threats, achieving high accuracy and superior efficien…

RansomTrack introduces a hybrid behavioral analysis framework that combines static and dynamic feature extraction to achieve high-accuracy, low-latency, and explainable real-time ransomware detection.

This paper proposes a hybrid CNN-LSTM framework to enhance cyber attack detection and prevention in U.S. critical digital infrastructure by evaluating multiple machine learning models on the CSE-CIC-I…

By analyzing over 27,000 posts from 325 public ransomware leak sites, this paper demonstrates that ransomware groups exhibit non-random, predictable operational regularities concerning victim concentr…

This paper empirically evaluates the use of Retrieval-Augmented Generation (RAG) for malware explanation and finds that RAG frequently degrades explanation quality by adding noise when structured secu…

eDySec introduces a deep learning framework for dynamic behavioral analysis that significantly improves the detection of malicious software packages in the PyPI ecosystem by enhancing stability and ex…

The paper proposes a privacy-aware machine unlearning framework using SISA training to efficiently remove the influence of specific training data from RL-based ransomware detectors with minimal perfor…

The paper introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

DeepXplain introduces an explainable deep reinforcement learning framework that enhances the trustworthiness and effectiveness of autonomous cyber defense against multi-stage APT campaigns by integrat…

This paper proposes an improved CNN-LSTM model for IoT intrusion detection, achieving high accuracy by combining spatial and temporal feature learning from network traffic.

The paper introduces Trident, a novel malware detection system that combines static features, LLM-derived behavioral rules, and direct LLM analysis to achieve superior robustness against concept drift…

This paper proposes and evaluates the KAN-LSTM model, demonstrating that Kolmogorov-Arnold Networks (KANs) significantly outperform traditional deep learning models for accurate and parameter-efficien…

This paper proposes an explainability-guided adversarial attack that successfully evades transformer-based malware detectors by perturbing the most influential components of the control flow graph rep…

The paper demonstrates that an attention-augmented LSTM model can achieve near-perfect character-level decipherment of homophonic ciphertexts from historical English and Swedish, even under challengin…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

The paper demonstrates that static malware classifiers often rely on superficial artifacts like packing and metadata rather than true malicious semantics, using the TRUSTEE interpretability tool to di…

NLLog introduces a lightweight system that converts structured security logs into natural language sentences for improved anomaly detection, achieving high performance with low false-positive rates su…

NLLog is a lightweight pipeline that rewrites system-generated logs into natural language for improved analysis and comprehension.

SeqShield proposes a behavior-based rootkit detection system for Windows by analyzing API call sequences using n-gram features, achieving high detection accuracy even against mutated malware variants.

AsmRAG is a novel framework that improves malware detection by treating it as an evidence-based retrieval task using a code-specialized LLM, achieving high accuracy while providing transparent forensi…