ransomwhere

Ransomwhere A Proof of Concept ransomware sample that encrypts your files to test out your ransomware detection & prevention strategies. If no arguments are provided, ransomwherewill automatically execute the encrypt mode without deleting the original files. I am not responsible for any damage...

Malware

YARA rules, programs for ransomware families: Akira Babuk Beast BlackByte BlackCat ALPHV BlackMatter BlueLocker MemeCryptor Cerber Conti DarkBit DeadBolt Dharma Enmity GarrantyDecrypt GlobeImposter / NewBomani GoodMorning / SD HardBit Hive / Hunters International HsHarada / Rapture Hunters...

KilledProcessCanary

Windows Killed Process Canary A Windows Service to detect if it has been shutdown and hibernate if multiple instances are. Released as open source by NCC Group Plc - http://www.nccgroup.com/ Developed by Ollie Whitehouse, ollie dot whitehouse at nccgroup dot com...

detect-ransomware-filenames

Detect Ransomware Filenames This package/script watches SMB transactions to look for known bad filenames that ransomware is known to use. It rides on top of the Anti-Ransomware File System Resource Manager Lists maintained here. How to use A Python script download-list.py is included to be able t...

ESXi-Ransomware-Scanner-mi

ESXiransomwarescanner A program that scans IP addresses for signs of ESXi compromise by grabbing the ransom note from an html page and comparing the strings. Requirements python 3.x requests BeautifulSoup tqdm colorama Installation Use the package manager pip to install the required packages. pip...

melee

MELEE: Una herramienta para detectar infecciones de ransomware en instancias MySQL Los atacantes están abusando de las instancias MySQL para llevar a cabo operaciones nefastas en Internet. Los ciberdelincuentes están apuntando a instancias MySQL expuestas y desencadenando infecciones a gran escal...

strafer

Strafer Las infecciones en Elasticsearch están aumentando exponencialmente. Los adversarios están explotando interfaces de Elasticsearch abiertas y expuestas para desencadenar infecciones en implementaciones en la nube y fuera de ella. Durante esta charla, lanzaremos una herramienta llamada...

Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution

Ransomware detection and family attribution require analysis of different modalities because it can use packing, obfuscation, process manipulation and runtime evasion techniques. However, conventional multimodal usually uses all available modalities for every sample resulting in unnecessary...

SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design

Ransomware detection is a security-critical task in which false negatives and false positives have unequal operational consequences. Conventional machine learning detectors often use symmetric objectives that penalize missed ransomware detections and benign false alarms equally, although a false...

Agentic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection

Ransomware has evolved into a complex, adaptive, and fast-moving adversary category in which static signatures and monolithic classifiers fail to generalise under concept drift, evasion, and behavioural polymorphism. In this paper, we present Agentic SABRE Semantic-Behavioural Arbitration for...

TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats

Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits the effectiveness of static or predefined models. To address this challenge, we propose TL-RL-FusionNet, a reinforceme...

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

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...

Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection

Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and responsible AI deployment demand not only accurate detection but also the ability to efficiently remove the influence...

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ransomware is the most reported cybercrime in terms of financial damage, highlighting the urgent need for early-stage detection before encryption is...

Automating Cloud Security and Forensics through a Secure-By-Design Generative AI Framework

As cloud environments become increasingly complex, cybersecurity and forensic investigations must evolve to meet emerging threats. Large Language Models LLMs have shown promise in automating log analysis and reasoning tasks, yet they remain vulnerable to prompt injection attacks and lack forensic...

An overview of ransomware threats in Japan in 2025 and early detection insights from Qilin cases

In 2025, a total of 134 ransomware incidents were reported in Japan, marking a 17.5% increase compared to 2024. Among these, 22 incidents were attributed to Qilin, representing 16.4% of the total. In 2025, Qilin ransomware was highly active. Looking ahead to 2026, unless there is significant...

Malware and Ransomware Detection in M365

Availability Requirement Threat Detection is available to Veeam Data Cloud for Microsoft 365 customers with Premium or Advanced plans. Customers must opt in to AI settings to enable this feature. Contact your Veeam account team or see your plan details to confirm availability. Supported Workloads...

CVE-2025-62000: Incomplete Comparison with Missing Factors

BullWall Ransomware Containment may not always detect an encrypted file. This issue affects a specific file inspection method that evaluates file content based on header bytes. An authenticated attacker could encrypt files, preserving the first four bytes and preventing this particular method fro...

BullWall Ransomware Containment and Server Intrusion Protection multiple vulnerabilities

RISK EVALUATION BullWall Ransomware Containment and Server Intrusion Protection are products used for ransomware containment. Multiple vulnerabilities were reported that when used individually or in conjunction could allow a remote attacker with valid credentials to log in to a system with...

Interpretable Ransomware Detection Using Hybrid Large Language Models: A Comparative Analysis of BERT, RoBERTa, and DeBERTa through LIME and SHAP

Ransomware continues to evolve in complexity, making early and explainable detection a critical requirement for modern cybersecurity systems. This study presents a comparative analysis of three Transformer-based Large Language Models LLMs BERT, RoBERTa, and DeBERTa for ransomware detection using...