Results 41 to 50 of about 1,222 (160)

A Survey on Detection Techniques for Cryptographic Ransomware

IEEE Access, 2019
Crypto-ransomware is a type of malware that encrypts user files, deletes the original data, and asks for a ransom to recover the hijacked documents. It is a cyber threat that targets both companies and residential users, and has spread in recent years ...
Eduardo Berrueta, Daniel Morato, Eduardo Magana, Mikel Izal   +3 more
doaj   +1 more source

Ransomware Detection Model Based on Adaptive Graph Neural Network Learning

Applied Sciences
Ransomware is a type of malicious software that encrypts or locks user files and demands a high ransom. It has become a major threat to cyberspace security, especially as it continues to be developed and updated at exponential rates. Ransomware detection
Jun Li, Gengyu Yang, Yanhua Shao
doaj   +1 more source

XGBoost-Powered Ransomware Detection

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Ransomware remains a rapidly evolving cyber threat, causing substantial financial and operational disruptions globally. Traditional signature-based detection systems are ineffective against sophisticated, zero-day attacks due to their static nature. Consequently, machine learning-based approaches offer a more effective and adaptive alternative.
Wildanil Ghozi, Heru Lestiawan, Ramadhan Rakhmat Sani, Jassim Nadheer Hussein, Fauzi Adi Rafrastara   +4 more
openaire   +1 more source

Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help ...
Chandramohan Dhasaratha, Mohammad Kamrul Hasan, Shayla Islam, Shailesh Khapre, Salwani Abdullah, Taher M. Ghazal, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Nguyen Vo, Md Akhtaruzzaman   +9 more
wiley   +1 more source

A Survey of Ransomware Detection Methods

IEEE Access
Ransomware attacks continue to pose a significant challenge to cybersecurity, causing substantial financial and reputational damage to individuals and organizations. These attacks typically encrypt user data and demand a ransom for its release.
Saleh Alzahrani, Yang Xiao, Sultan Asiri, Jianying Zheng, Tieshan Li   +4 more
doaj   +1 more source

Detecting Ransomware with Honeypot Techniques [PDF]

2016 Cybersecurity and Cyberforensics Conference (CCC), 2016
Attacks of Ransomware are increasing, this form of malware bypasses many technical solutions by leveraging social engineering methods. This means established methods of perimeter defence need to be supplemented with additional systems. Honeypots are bogus computer resources deployed by network administrators to act as decoy computers and detect any ...
openaire   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Ransomware deployment methods and analysis: views from a predictive model and human responses

Crime Science, 2019
Ransomware incidents have increased dramatically in the past few years. The number of ransomware variants is also increasing, which means signature and heuristic-based detection techniques are becoming harder to achieve, due to the ever changing pattern ...
Gavin Hull, Henna John, Budi Arief
doaj   +1 more source

Understanding the Autonomous Electric Vehicle Cyber Threat Landscape: A Focus on Infrastructure, Threats and Ontology‐Based Modelling

Energy Internet, EarlyView.
ABSTRACT The development of autonomous electric vehicles (AEVs) represents the convergence of two simultaneous automotive revolutions: electric vehicles (EVs) and autonomous vehicles (AVs). AVs require sensors, decision‐making systems and actuation systems to achieve autonomous driving, whereas EVs require intelligent management and real‐time ...
Ohud Alsadi, Abdulrahman Almijlad, Trinadh Pamulapati, Rajiv Ranjan, Devki Jha, Tejal Shah   +5 more
wiley   +1 more source

MIRAD: A Method for Interpretable Ransomware Attack Detection

IEEE Access
In the face of escalating crypto-ransomware attacks, we introduce MIRAD, a novel dynamic detection method. MIRAD leverages machine learning to continuously monitor API calls and registry entries, detecting ransomware at all stages of infection while ...
Bartosz Marcinkowski, Maja Goschorska, Natalia Wilenska, Jakub Siuta, Tomasz Kajdanowicz   +4 more
doaj   +1 more source