Results 61 to 70 of about 31,501 (227)

Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model

IEEE Access, 2023
Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low.
Ghadah Aldehim, Munya A. Arasi, Majdi Khalid, Sumayh S. Aljameel, Radwa Marzouk, Heba Mohsen, Ishfaq Yaseen, Sara Saadeldeen Ibrahim   +7 more
doaj   +1 more source

Securing the Unseen: A Comprehensive Exploration Review of AI‐Powered Models for Zero‐Day Attack Detection

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, Nuruzzaman Faruqui, Akm Azad, Mohammad Ali Moni   +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, Mohamadreza Fereydooni, Monireh Abdoos, Mojtaba Vahidi-Asl   +3 more
openalex   +1 more source

Microsoft Malware Classification Challenge

, 2018
The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour.
Ronen, Royi, Radu, Marian, Feuerstein, Corina, Yom-Tov, Elad, Ahmadi, Mansour   +4 more
openaire   +2 more sources

Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models

Applied Sciences, 2021
There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks.
Walid El-Shafai, Iman Almomani, Aala AlKhayer   +2 more
doaj   +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, A. Couce‐Vieira, D. Arroyo, D.R. Insua   +3 more
wiley   +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., Doak, Justin E., Draelos, Timothy J., Ingram, Joe B., James, Conrad D., Lamb, Christopher C., Smith, Michael R.   +6 more
core   +1 more source

CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments

Sensors
Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This
Mainak Basak, Myung-Mook Han
doaj   +1 more source

Android malware detection method based on deep neural network

网络与信息安全学报, 2020
Android is increasingly facing the threat of malware attacks. It is difficult to effectively detect large-sample and multi-class malware for traditional machine learning methods such as support vector machine, method for Android malware detection and ...
CHAO Fan, YANG Zhi, DU Xuehui, SUN Yan
doaj   +1 more source

Similarity-Based Malware Classification Using Graph Neural Networks

Applied Sciences, 2022
This work proposes a novel malware identification model that is based on a graph neural network (GNN). The function call relationship and function assembly content obtained by analyzing the malware are used to generate a graph that represents the ...
Yu-Hung Chen, Jiann-Liang Chen, Ren-Feng Deng   +2 more
doaj   +1 more source