Results 1 to 10 of about 16,189 (118)
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
Automated Android Malware Detection Using User Feedback. [PDF]
Sensors (Basel), 2022 The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed
Duque J +4 more
europepmc +5 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 +6 more sources
Klasifikasi Malware Family menggunakan Metode k-Nearest Neighbor (k-NN) [PDF]
, 2021 Smartphones based on Android OS have the most users today because they are comfortable to use and offer a variety of features. As a result, many malware developers have made Android OS their main target. Every year, new types of malware families emerge
Akbi, Denar Regata +2 more
core +1 more source
Benchmarking Android malware analysis tools
Electronics, 2023 Abstract Today, malware is arguably one of the biggest challenges organizations face from a cybersecurity standpoint, regardless of the types of devices used in the organization. One of the most malware-attacked mobile operating systems today is Android.
Javier Bermejo Higuera +5 more
openaire +1 more source
Android Malware Characterization using Metadata and Machine Learning Techniques [PDF]
, 2017 Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to ...
Guzmán, Antonio +3 more
core +2 more sources
Android Platform Malware Analysis [PDF]
International Journal of Advanced Computer Science and Applications, 2015 Mobile devices have evolved from simple devices, which are used for a phone call and SMS messages to smartphone devices that can run third party applications. Nowadays, malicious software, which is also known as malware, imposes a larger threat to these mobile devices.
Khalid Alfalqi +2 more
openaire +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 ...
McLaughlin, Niall; id_orcid 0000-0002-0917-9145 +10 more
openaire +4 more sources
KLASIFIKASI MALWARE ANDROID DENGAN MENGGUNAKAN METODE CATBOOST ALGORITMA [PDF]
, 2023 In 2008, Android was introduced as a popular open source project due to its customizability and low hardware requirements. Mid-2021 statistics from GlobalStat Counter shows that Android dominates the mobile operating system market with 72.74%.
IRSYADUDDIN, YUSUF
core
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