Results 61 to 70 of about 2,904 (224)

Android Malware Detection based on Factorization Machine

, 2018
With the increasing popularity of Android smart phones in recent years, the amount of Android malware is growing rapidly. Due to its great threat and damage to mobile phone users, Android malware detection has become increasingly important in cyber ...
Li, Chenglin
core   +1 more source

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

AI‐Powered Defense: Leveraging Deep Learning for Effective Malware Detection

Applied Computational Intelligence and Soft Computing, Volume 2026, Issue 1, 2026.
Traditional malware detection techniques frequently fail to detect and stop malicious activity in an era where cyber threats are becoming more complex. Any software that enters a computer system without the administrator’s consent is considered malicious software.
Nancy Awadallah Awad, Dimitrios A. Karras   +1 more
wiley   +1 more source

Applying Bayesian probability for Android malware detection using permission features

, 2021
he tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users.
Mohd Nizam, Mohmad Kahar, Mohd Faizal, Ab Razak, Sharfah Ratibah, Tuan Mat, Juliza, Mohamad Arif, Azlee, Zabidi   +4 more
core   +1 more source

Robust AI‐SCORE Framework: Independent and Adversarial Validation for Malware Detection

Journal of Engineering, Volume 2026, Issue 1, 2026.
Traditional malware detection methods such as signature‐based approaches and statistical analysis are becoming less effective in detecting the new breed of malware, which is holding high levels of complexity in terms of the number of code versions, compilation patterns, time to live (TTL), and jumping through evasion techniques.
Hafiz Talha Arif Zuberi, Usman Ali, Usama Ahmed, Muhammad Usman Tariq, Tariq Shahzad, Muhammad Adnan Khan, Amir H. Gandomi, Luis Rabelo   +7 more
wiley   +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

Securing End‐To‐End Encrypted File Sharing Services With the Messaging Layer Security Protocol

Concurrency and Computation: Practice and Experience, Volume 37, Issue 27-28, 25 December 2025.
ABSTRACT Secure file sharing is essential in today's digital environment, yet many systems remain vulnerable: if an attacker steals client keys, they can often decrypt both past and future content. To address this challenge, we propose a novel file‐sharing architecture that strengthens post‐compromise security while remaining practical.
Roland Helmich, Lars Braubach
wiley   +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

AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks

Advanced Intelligent Discovery, Volume 1, Issue 2, August 2025.
This paper describes the basis for AInsectID Version 1, a GUI‐operable open‐source insect species identification, color processing, and image analysis software. This paper discusses our methods of algorithmic development, coupled to rigorous machine training used to enable high levels of validation accuracy.
Haleema Sadia, Parvez Alam
wiley   +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