Results 91 to 100 of about 2,335 (206)
Machine learning for Android ransomware detection
, 2021 Effective detection of ransomware is becoming increasingly important due to the influx of smartphones and the increasing amount of personal data being stored in smartphones.
Bagui, Sikha (author) +1 more
core +1 more source
Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis. [PDF]
Sensors (Basel), 2023 Albin Ahmed A +5 more
europepmc +1 more source
Ransomware Detection and Classification using Machine Learning
, 2023 Vicious assaults, malware, and various ransomware pose a cybersecurity threat, causing considerable damage to computer structures, servers, and mobile and web apps across various industries and businesses.
Kunku, Kavitha, Roy, Kaushik, Zaman, ANK
core
Effective Ransomware Detection Using Entropy Estimation of Files for Cloud Services. [PDF]
Sensors (Basel), 2023 Lee K, Lee J, Lee SY, Yim K.
europepmc +1 more source
Zero-day attack and ransomware detection
CoRRThis 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
Ransomware : a system centric detection approach
, 2017 There are three approaches taken to analyze defenses against ransomware: signature based patterns, similar to virus detection; observing execution behavior, such as deleting a large number of files and changing file types; and a data-centric method ...
Cromis, Brian R.
core
Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms. [PDF]
Sensors (Basel), 2023 Herrera-Silva JA, Hernández-Álvarez M.
europepmc +1 more source
A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
Future InternetRansomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy
Mazen Gazzan +3 more
doaj +1 more source
Machine Learning-Based Static Ransomware Detection Using PE Header Features and SHAP Interpretation
Journal of Cybersecurity and PrivacyCybercriminals 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
, 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