Results 21 to 30 of about 582,811 (354)
LinRegDroid: Detection of Android Malware Using Multiple Linear Regression Models-Based Classifiers
IEEE Access, 2022 In this study, a framework for Android malware detection based on permissions is presented. This framework uses multiple linear regression methods. Application permissions, which are one of the most critical building blocks in the security of the Android
Durmuş Özkan Şahın +2 more
semanticscholar +1 more source
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
IEEE Transactions on Information Forensics and Security, 2019 With the widespread use of smartphones, the number of malware has been increasing exponentially. Among smart devices, android devices are the most targeted devices by malware because of their high popularity.
Taeguen Kim +4 more
semanticscholar +1 more source
Ilkom Jurnal Ilmiah, 2017 Sekolah Garis Depan (SGD) merupakan sekolah yang terletak di daerah terluar, terpencil, dan tertinggal (3T), propinsi Gorontalo yang merupakan propinsi baru pemekaran dari Sulawesi Utara masih memiliki ±25 sekolah yang termasuk sekolah 3T.
Abdul Malik Ibuna
doaj +1 more source
DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis
Applied Sciences, 2020 Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of ...
Xin Su +5 more
doaj +1 more source
Security Code Smells in Android ICC [PDF]
, 2018 Android Inter-Component Communication (ICC) is complex, largely unconstrained, and hard for developers to understand. As a consequence, ICC is a common source of security vulnerability in Android apps.
Frischknecht, Patrick +3 more
core +2 more sources
Deep Reinforcement Learning for Black-box Testing of Android Apps [PDF]
ACM Transactions on Software Engineering and Methodology, 2021 The state space of Android apps is huge, and its thorough exploration during testing remains a significant challenge. The best exploration strategy is highly dependent on the features of the app under test.
Andrea Romdhana +3 more
semanticscholar +1 more source
Disaster Resilience Education and Research Roadmap for Europe 2030 : ANDROID Report [PDF]
, 2015 A disaster resilience education and research roadmap for Europe 2030 has been launched. This roadmap represents an important output of the ANDROID disaster resilience network, bringing together existing literature in the field, as well as the results of ...
Amaratunga, Dilanthi +11 more
core +1 more source
Significant Permission Identification for Machine-Learning-Based Android Malware Detection
IEEE Transactions on Industrial Informatics, 2018 The alarming growth rate of malicious apps has become a serious issue that sets back the prosperous mobile ecosystem. A recent report indicates that a new malicious app for Android is introduced every 10 s.
Jin Li +5 more
semanticscholar +1 more source
Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware
Conference on Computer and Communications Security, 2020 Machine learning (ML) classifiers have been widely deployed to detect Android malware, but at the same time the application of ML classifiers also faces an emerging problem.
Xiaohan Zhang +7 more
semanticscholar +1 more source
Detection and Prevention of Malware in Android Operating System
Mehran University Research Journal of Engineering and Technology, 2021 The Internet is not safe anymore, malware can be discovered anywhere on the Internet. The risk of malware has increased also due to the increasing popularity and use of Smartphones and their underlying cost-free applications. With its great market share,
Kashif Ali Dahri +2 more
doaj +1 more source