Results 61 to 70 of about 11,847 (201)

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

, 2018
Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary.
Chen, Xiao, Li, Chaoran, Nepal, Surya, Ren, Kui, Wang, Derui, Wen, Sheng, Xiang, Yang, Zhang, Jun   +7 more
core   +1 more source

Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

Engineering Reports, Volume 8, Issue 1, January 2026.
Overview of the proposed RF–ViT ensemble for multi‐class malware classification. Textual (BoW/byte‐frequency) and visual representations are combined via a product rule, achieving improved accuracy and robustness over individual models. ABSTRACT Malware remains a significant concern for modern digital systems, increasing the need for reliable and ...
Fadi Makarem, Rida El Chall, Abed Ellatif Samhat, Khouloud Samrouth, Nader Bakir   +4 more
wiley   +1 more source

Android Fragmentation in Malware Detection

Computers & Security, 2019
Abstract Differences between Android versions affect not only application developers but also make the task of securing Android harder, as it is not easy to keep track of updates. In this paper, we first systematically analyze the Android framework, which includes APIs and enforced manifest permissions to realize the inconsistency currently exists in
Long Nguyen-Vu, Jinung Ahn, Souhwan Jung
openaire   +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

Usability Evaluation of a Push‐Based Passwordless Authentication Model Using Public‐Key Cryptography

IET Biometrics, Volume 2026, Issue 1, 2026.
Despite major advancements in the sphere of the public‐key authentication specifically in the instances of the newly established standards like WebAuthn and the FIDO2, the practical implementation of the passwordless login systems is still hindered by the usability factors, platform‐related requirements, and the very nature of the deployment process is
Ghulam Mustafa, Shah Zaman Nizamani, Irfana Memon, Asif Ali Wagan, Solomiia Fedushko, Abdullah Al Hejaili, M. Srinivas   +6 more
wiley   +1 more source

Malware detection techniques for mobile devices

, 2017
Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications
Amro, Bela
core   +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

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

A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

, 2017
Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection.
Chandramohan, Mahinthan, Chen, Lihui, Liu, Yang, Narayanan, Annamalai   +3 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