Results 81 to 90 of about 1,349 (172)

eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance

Sensors
Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model.
Abdullah Alqahtani, Frederick T. Sheldon
doaj   +1 more source

Detecting Ransomware

, 2018
Ransomware attacks -- in contrast to other cyber attacks -- must not be detected and especially not blocked or recovered on first sight. This relaxation is supported by the rareness of ransomware attacks. Certainly, the uprising of ransomware families, which are able to circumvent the detection mechanism, integrated into the local machine, prevents the
openaire   +1 more source

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

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

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

Ransomware Detection with Deep Neural Networks

Proceedings of the 8th International Conference on Information Systems Security and Privacy, 2022
Matan Davidian, Natalia Vanetik, Michael Kiperberg   +2 more
openaire   +1 more source

Ensemble machine learning for proactive android ransomware detection using network traffic. [PDF]

Sci Rep
Kirubavathi G, Padma Mayuri B, Pranathasree S, Alagappan R, Ajayan A, Ismael WM, Rehman AU.   +6 more
europepmc   +1 more source

An Intelligent Sensing Framework for Early Ransomware Detection Using MHSA-LSTM Machine Learning. [PDF]

Sensors (Basel)
Alqahtani A, Ohemeng MO, Sheldon FT.
europepmc   +1 more source

A novel technique for ransomware detection using image based dynamic features and transfer learning to address dataset limitations. [PDF]

Sci Rep
Ferdous J, Islam R, Mahboubi A, Islam MZ.   +3 more
europepmc   +1 more source