Results 61 to 70 of about 4,236,332 (208)
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 +4 more
wiley +1 more source
Windows Malware Detection Under the Machine Learning Models and Neutrosophic Numbers [PDF]
Neutrosophic Sets and SystemsSignificant cybersecurity risks are posed by malware assaults on Windows computers, which call for efficient detection and prevention systems.
Alber S. Aziz +5 more
doaj +1 more source
Malfustection: Obfuscated Malware Detection and Malware Classification with Data Shortage by Combining Semi-Supervised and Contrastive Learning [PDF]
, 2021 Mohammad Mahdi Maghouli +3 more
openalex +1 more source
Italian National Conference on Sensors, 2022 Android has become the leading mobile ecosystem because of its accessibility and adaptability. It has also become the primary target of widespread malicious apps. This situation needs the immediate implementation of an effective malware detection system.
Farhan Ullah +5 more
semanticscholar +1 more source
The Journal of World Intellectual Property, EarlyView.Abstract Understanding the role of information communication technologies (ICTs) in development, especially in relation to marginalized populations, has been the focus of many related disciplinary categories within the broader ecosystem of information sciences.
Chidi Oguamanam
wiley +1 more source
Enhanced Metamorphic Techniques-A Case Study Against Havex Malware
IEEE Access, 2021 Most of the commercial antiviruses are signature based, that is, they use existing database signature to detect the malware. Malware authors use code obfuscation techniques in their variety of malware with the aim of bypassing detection by antiviruses ...
Zainub Mumtaz +4 more
doaj +1 more source
Expert Systems, Volume 43, Issue 3, March 2026.ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam +3 more
wiley +1 more source
Android Mobile Malware Detection Using Machine Learning: A Systematic Review
Electronics, 2021 With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share.
J. M. D. Senanayake +2 more
semanticscholar +1 more source
A cybersecurity risk analysis framework for systems with artificial intelligence components
International Transactions in Operational Research, Volume 33, Issue 2, Page 798-825, March 2026.Abstract The introduction of the European Union Artificial Intelligence (AI) Act, the NIST AI Risk Management Framework, and related international norms and policy documents demand a better understanding and implementation of novel risk analysis issues when facing systems with AI components: dealing with new AI‐related impacts; incorporating AI‐based ...
J.M. Camacho +3 more
wiley +1 more source
BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices [PDF]
PeerJ Computer ScienceDue to the increase in the volume and diversity of malware targeting Android systems, research on detecting this harmful software is steadily growing. Traditional malware detection studies require significant human intervention and resource consumption ...
Emre Şafak +3 more
doaj +2 more sources