Results 71 to 80 of about 2,905 (223)

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

DyBAnd: Dynamic Behavior Based Android Malware Detection [PDF]

, 2023
Android is the most popular widely accessible smartphone operating system, yet its permission declaration and access control systems cannot detect malicious activities. Advanced malware uses cutting-edge obfuscation techniques to mask its true intentions
Sihag, Vikas, Jaiswal, Shashank, Dragoni, Nicola, Choudhary, Gaurav   +3 more
core   +1 more source

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  

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

Cipher‐Guard: A Machine Learning Model for Adaptive and Context‐Aware Password Security

IET Information Security, Volume 2026, Issue 1, 2026.
This research aims to enhance password security by designing, training and testing Cipher‐Guard, a machine learning (ML) algorithm that incorporates complex features and techniques derived from proven feature engineering. The current model is inherently built on the equations of mathematical modelling concerning complexity metrics of passwords and ...
Mohammed Naif Alatawi, Xueqin Liang
wiley   +1 more source

Extracting Android Applications Data for Anomaly-based Malware Detection [PDF]

, 2015
In order to apply any machine learning algorithm or classifier, it is fundamentally important to first and foremost collect relevant features. This is most important in the field of dynamic analysis approach to anomaly malware detection systems. In this
Ume U.A, Abdullahi M.B, Waziri O.V., J. Aba, Adewale O.S   +4 more
core   +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 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

Android Malware Detection Based on Deep Learning: Achievements and Challenges [PDF]

, 2020
With the prosperous of Android applications, Android malware has been scattered everywhere, which raises the serious security risk to users. On the other hand, the rapid developing of deep learning fires the combat between the two sides of malware ...
Chen, Yi, Zou, Wei, Tang, Di
core   +1 more source