Results 51 to 60 of about 2,904 (224)

A Comprehensive Study of Malware Detection in Android Operating Systems

, 2021
Android is now the world\u27s (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available.
Ahmed, Omar M., Kak, Shakir Fattah, Ibrahim, Ibrahim Mahmood, Omar, Naaman, Ibrahim, Rowaida Khalil, Rashid, Zryan Najat, Salih, Azar Abid, Hamdi, Suhaib Jasim, Ahmed, Awder Mohammed, Yasin, Hajar Maseeh   +9 more
core   +1 more source

Android Malware Detection: Looking beyond Dalvik Bytecode [PDF]

, 2021
peer reviewedMachine learning has been widely employed in the literature of malware detection because it is adapted to the need for scalability in vetting large scale samples of Android.
Tiezhu Sun, Nadia Daoudi, DAOUDI, Nadia, BISSYANDE, Tegawendé François D Assise, ALLIX, Kevin, Tegawende F. Bissyande, SUN, Tiezhu, Kevin Allix   +7 more
core   +1 more source

Runtime Detection Framework for Android Malware [PDF]

Mobile Information Systems, 2018
As the number of Android malware has been increased rapidly over the years, various malware detection methods have been proposed so far. Existing methods can be classified into two categories: static analysis-based methods and dynamic analysis-based methods.
TaeGuen Kim 0002, Boojoong Kang, Eul Gyu Im   +2 more
openaire   +1 more source

Dataset Bias in Android Malware Detection

CoRR, 2022
Researchers have proposed kinds of malware detection methods to solve the explosive mobile security threats. We argue that the experiment results are inflated due to the research bias introduced by the variability of malware dataset. We explore the impact of bias in Android malware detection in three aspects, the method used to flag the ground truth ...
Yan Lin, Tianming Liu 0002, Wei Liu, Zhigaoyuan Wang, Li Li 0029, Guoai Xu, Haoyu Wang 0001   +6 more
openaire   +2 more sources

API Sequences based Malware Detection for Android

, 2015
To mitigate security problem brought by Android malware, various work has been proposed such as behavior based malware detection and data mining based malware detection.
Zhong Chen, Zhi Guan, Jiawei Zhu, Wu, Zhengang, Zhengang Wu, Chen, Zhong, Guan, Zhi, Zhu, Jiawei   +7 more
core   +1 more source

AntiWare: An automated Android malware detection tool based on machine learning approach and official market metadata

, 2016
The prevalence of mobile devices has increased rapidly in recent years. People store valuable data like personal and financial information on those devices.
Kamil Akhuseyinoglu, Akhuseyinoglu, Kamil, Akhuseyinoglu, Nuray Baltaci, Nuray Baltaci Akhuseyinoglu   +3 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

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

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  

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