Results 101 to 110 of about 2,945,918 (205)

Ransomware detection

, 2021
Ransomware is classified as a category of malicious software (often reffered to as malware), which is designed to perform changes on the targeted sytem (e.g. encrypt certain files), and subsequently demand a payment of ransom, which is promised to result
Chadima, Vojtěch
core   +1 more source

Zero-day attack and ransomware detection

CoRR
This work is part of a master's in information technology (MIT) at the University of Pretoria, Faculty of ...
Steven Jabulani Nhlapo, Mike Wa Nkongolo
openaire   +2 more sources

Machine Learning-Based Static Ransomware Detection Using PE Header Features and SHAP Interpretation

Journal of Cybersecurity and Privacy
Cybercriminals use advanced techniques to launch an attack against organizations, which causes disruption of normal business activities. The traditional signature-based malware detection methods are not effective in the detection of ransomware. Therefore,
Gabryella Barnes, Ahmad Ghafarian
doaj   +1 more source

Machine learning-based ransomware classification of Bitcoin transactions

Journal of King Saud University: Computer and Information Sciences
Ransomware presents a significant threat to the security and integrity of cryptocurrency transactions. This research paper explores the intricacies of ransomware detection in cryptocurrency transactions using the Bitcoinheist dataset.
Omar Dib, Zhenghan Nan, Jinkua Liu
doaj   +1 more source

Machine learning for Ransomware detection

Este trabalho apresenta o desenvolvimento de um protótipo de ransomware, seguido pela criação e aplicação de um modelo de aprendizado de máquina baseado em floresta aleatória para a detecção de ransomwares.
Breda, Ian Marques [UNESP]
core  

Radar: a realistic dataset for advancing ransomware detection

Cybersecurity
Ransomware threats are growing in frequency and severity, posing significant challenges to cybersecurity defences. Machine learning (ML) has gained attention as a promising tool for detecting ransomware, but the lack of realistic ransomware datasets for ...
Jamil Ispahany, Oscar Blessed Deho, Md Rafiqul Islam, M. Arif Khan, Md Zahidul Islam   +4 more
doaj   +1 more source

Deep MCRan: Hybrid Analysis-Based Ransomware Detection Using Multi-Channel Data Fusion

IEEE Access
Ransomware has emerged as one of the most destructive cybersecurity threats, causing substantial financial losses and operational disruptions. Attackers increasingly employ obfuscation and evasion techniques to bypass traditional security mechanisms ...
Sibel Gulmez, Arzu Gorgulu Kakisim, Ibrahim Sogukpinar   +2 more
doaj   +1 more source

Behavioral Analysis of Ransomware Families and Their Temporal Evolution

reservedRansomware is a well-known type of malware that has existed for many years. Its use and impact have increased significantly in recent times, with modern ransomware attacks causing severe financial and operational damage to organizations and ...
BOSCHIERO, LUCA
core  

Local And Network Ransomware Detection Comparison

, 2019
Background. Ransomware is a malicious application encrypting important files on a victim's computer. The ransomware will ask the victim for a ransom to be paid through cryptocurrency. After the system is encrypted there is virtually no way to decrypt the
Ahlgren, Filip
core   +1 more source

Constructing Resilient Frameworks for Autonomous Ransomware Detection Using Algorithmic Behavior Profiling

Ransomware has become a significant cybersecurity threat, leveraging advanced encryption techniques to disrupt critical systems and extort payments.
Oscar Risko, Oliver Tallisford, Penelope Greenborough, Samuel Rotherham   +3 more
core   +1 more source