Results 41 to 50 of about 978 (166)

Android Malware Detection Technology Based on Deep Convolutional Neural Network

四川大学学报. 自然科学版, 2020
The rapid iteration of the Android system and its open source features have resulted in many variants of Android malware, which brings great challenges to the classification and detection of Android malware.
GAO Yang-Chen, Fang Yong, Liu Liang, ZHANG Lei   +3 more
doaj  

Android Malware Detection Using BERT

, 2022
Android malware detection using ...
SOUANI, Badr, KHANFIR, Ahmed, BARTEL, Alexandre, ALLIX, Kevin, LE TRAON, Yves   +4 more
openaire   +2 more sources

A Novel Neural Network Architecture Using Automated Correlated Feature Layer to Detect Android Malware Applications

Mathematics, 2023
Android OS devices are the most widely used mobile devices globally. The open-source nature and less restricted nature of the Android application store welcome malicious apps, which present risks for such devices.
Amerah Alabrah
doaj   +1 more source

An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features

IEEE Access
With an increase in the number and complexity of malware, traditional malware detection methods such as heuristic-based and signature-based ones have become less adequate, leaving user applications vulnerable.
Abdurraheem Joomye, Mee Hong Ling, Muhammed Basheer Jasser, Athirah Mohd Ramly, Kok-Lim Alvin Yau   +4 more
doaj   +1 more source

Bioinspired artificial intelligence based android malware detection and classification for cybersecurity applications

Alexandria Engineering Journal
With the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps; thus, it develops the aim of malware that uses the vulnerability of ...
Shoayee Dlaim Alotaibi, Bayan Alabduallah, Yahia Said, Siwar Ben Haj Hassine, Abdulaziz A. Alzubaidi, Maha Alamri, Samah Al Zanin, Jihen Majdoubi   +7 more
doaj   +1 more source

Data Drift in Android Malware Detection

2024 International Conference on Machine Learning and Cybernetics (ICMLC)
Android malware detectors are now widely implemented with machine learning algorithms, trained on large datasets of goodware and malware applications gathered at a fixed moment in time. However, as recent work showed, this domain is not stationary, causing detectors to show degrading performance over time.
Minnei, Luca, Eddoubi, Hicham, Sotgiu, Angelo, Pintor, Maura, Demontis, Ambra, Biggio, Battista   +5 more
openaire   +2 more sources

Android Malware Detection Using Autoencoder

CoRR, 2019
9 Pages, 4 Figures, 3 ...
Abdelmonim Naway, Yuancheng Li
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Contaminant removal for Android malware detection systems [PDF]

2017 IEEE International Conference on Big Data (Big Data), 2017
2017 IEEE International Conference on Big ...
Lichao Sun 0001, Xiaokai Wei, Jiawei Zhang 0001, Lifang He 0001, Philip S. Yu, Witawas Srisa-an   +5 more
openaire   +2 more sources

Android malware detection as a Bi-level problem

Computers & Security, 2022
Malware detection is still a very challenging topic in the cybersecurity field. This is mainly due to the use of obfuscation techniques. To solve this issue, researchers proposed to extract frequent API (Application Programming Interface) call sequences and then use them as behavior indicators.
Jerbi, Manel, Chelly Dagdia, Zaineb, Bechikh, Slim, Said, Lamjed   +3 more
openaire   +3 more sources

A static analysis approach for Android permission-based malware detection systems.

PLoS ONE, 2021
The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial.
Juliza Mohamad Arif, Mohd Faizal Ab Razak, Suryanti Awang, Sharfah Ratibah Tuan Mat, Nor Syahidatul Nadiah Ismail, Ahmad Firdaus   +5 more
doaj   +1 more source