The paper introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

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 study provides a longitudinal analysis of dark web content, revealing that cybercrime discussions are dominated by a few persistent core topics rather than rapidly shifting themes.

The study analyzes coding patterns in malware versus benign software, finding that malware code is optimized for quick evasion and secrecy rather than maintainability, though its metrics are not uniqu…

The paper proposes a dynamic queueing framework that estimates an organization's cyber resources and attack surface dynamics by analyzing the timestamps of vulnerabilities and fixes, achieving high ac…

The paper proposes an explainable attention-based LSTM framework to achieve early and reliable detection of advanced, AI-assisted ransomware by analyzing file system behavioral sequences.

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…

This paper identifies a behavioral invariant in LLM agents under memory poisoning attacks, which can be used to detect such attacks with high accuracy.

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

The paper demonstrates that autonomous web agents are highly susceptible to social-engineering attacks, leaking critical PII even when they internally flag a site as suspicious, necessitating output-l…

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…

The paper introduces a queueing-theoretic framework to model dynamic cyber-attack surfaces, developing an adaptive reinforcement learning defense policy that significantly reduces active vulnerabiliti…

This paper demonstrates that YARA rules, even when stripped of metadata, contain enough stylistic information to accurately infer the original source repository, author, and even the malware family.

The paper argues that zero-day attacks primarily exploit undisclosed vulnerabilities rather than exhibiting novel behaviors, advocating for vulnerability-centric detection methods over purely behavior…

This study empirically measures the consistency and success rate of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation capabilit…

This study empirically measures the consistency and effectiveness of autonomous LLM penetration testing across multiple services, finding statistically significant differences in exploitation rates am…

This paper builds a multi-source log dataset with per-entry ATT&CK technique labels for detecting multi-stage cyberattacks using machine learning.

This paper systematically analyzes 123 publications on anti-forensics to quantify techniques and attack vectors, identify research patterns, and propose directions for a more coherent and ethical unde…

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…