Results 11 to 20 of about 1,195 (175)
Android malware analysis in a nutshell. [PDF]
PLoS One, 2022 This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems.
Almomani I, Ahmed M, El-Shafai W.
europepmc +4 more sources
An Analysis of Android Malware Classification Services. [PDF]
Sensors (Basel), 2021 The increasing number of Android malware forced antivirus (AV) companies to rely on automated classification techniques to determine the family and class of suspicious samples. The research community relies heavily on such labels to carry out prevalence studies of the threat ecosystem and to build datasets that are used to validate and benchmark novel ...
Rashed M, Suarez-Tangil G.
europepmc +5 more sources
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
Applied Sciences, 2022 As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware.
Abimbola G. Akintola +9 more
doaj +1 more source
Deep Android Malware Detection [PDF]
Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, 2017 In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing ...
Niall McLaughlin +10 more
openaire +3 more sources
Orchestrating Android Malware Experiments [PDF]
2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2019 Experimenting with Android malware requires to manipulate a large amount of samples and to chain multiple analyses. Scripting such a sequence of analyses on a large malware dataset becomes a challenge: the analysis has to handle fails on the computer and crashes on the used smartphone, in case of dynamic analyses.
Lalande, Jean-François +2 more
openaire +2 more sources
Android malicious attacks detection models using machine learning techniques based on permissions [PDF]
International Journal of Data and Network Science, 2023 The Android operating system is the most used mobile operating system in the world, and it is one of the most popular operating systems for different kinds of devices from smartwatches, IoT, and TVs to mobiles and cockpits in cars.
Mousa AL-Akhras +4 more
doaj +1 more source
Classification and Analysis of Android Malware Images Using Feature Fusion Technique
IEEE Access, 2021 The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market.
Jaiteg Singh +5 more
doaj +1 more source
Smart malware detection on Android [PDF]
Security and Communication Networks, 2015 AbstractNowadays, because of its increased popularity, Android is target to a growing number of attacks and malicious applications, with the purpose of stealing private information and consuming credit by subscribing to premium services. Most of the current commercial antivirus solutions use static signatures for malware detection, which may fail to ...
Laura Gheorghe +6 more
openaire +1 more source
A Hybrid Approach for Android Malware Detection and Family Classification.
International Journal of Interactive Multimedia and Artificial Intelligence, 2021 With the increase in the popularity of mobile devices, malicious applications targeting Android platform have greatly increased. Malware is coded so prudently that it has become very complicated to identify.
Meghna Dhalaria, Ekta Gandotra
doaj +1 more source