Results 61 to 70 of about 97,198 (223)
Mal-Detect: An intelligent visualization approach for malware detection
Journal of King Saud University: Computer and Information Sciences, 2022 Recent outbreaks of pandemics have deepened the adoption and use of IT-based systems. This development has led to an exponential increase in cyberattacks caused by malware.
Olorunjube James Falana +3 more
doaj +1 more source
Dynamic Analysis of Executables to Detect and Characterize Malware
, 2018 It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating ...
Aimone, James B. +6 more
core +1 more source
Journal of Computer Virology and Hacking Techniques, 2020 When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the
Basole, Samanvitha +2 more
openaire +3 more sources
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
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
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
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
An Open Source, Extensible Malware Analysis Platform
MATEC Web of Conferences, 2018 A malware (such as viruses, ransomware) is the main source of bringing serious security threats to the IT systems and their users now-adays. In order to protect the systems and their legitimate users from these threats, anti-malware applications are ...
Michalopoulos P. +3 more
doaj +1 more source
DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
Applied Sciences, 2023 Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher +4 more
doaj +1 more source
Android Malware Clustering through Malicious Payload Mining
, 2017 Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party ...
I Santos +7 more
core +1 more source