Malicious application detection in android—a systematic literature review
Context: In last decade, due to tremendous usage of smart phones it seems that these
gadgets became an essential necessity of day-to-day life. People are using new …
gadgets became an essential necessity of day-to-day life. People are using new …
MAPAS: a practical deep learning-based android malware detection system
A lot of malicious applications appears every day, threatening numerous users. Therefore, a
surge of studies have been conducted to protect users from newly emerging malware by …
surge of studies have been conducted to protect users from newly emerging malware by …
Android malware detection through hybrid features fusion and ensemble classifiers: The AndroPyTool framework and the OmniDroid dataset
Cybersecurity has become a major concern for society, mainly motivated by the increasing
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
number of cyber attacks and the wide range of targeted objectives. Due to the popularity of …
Constructing features for detecting android malicious applications: issues, taxonomy and directions
The number of applications (apps) available for smart devices or Android based IoT (Internet
of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill …
of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill …
Evodeep: a new evolutionary approach for automatic deep neural networks parametrisation
Abstract Deep Neural Networks (DNN) have become a powerful, and extremely popular
mechanism, which has been widely used to solve problems of varied complexity, due to their …
mechanism, which has been widely used to solve problems of varied complexity, due to their …
Malware: The never-ending arms race
H Menendez - Open Journal of Cybersecurity, 2021 - endsci.net
Abstract" Antivirus is death" and probably every detection system that focuses on a single
strategy for indicators of compromise. This famous quote that Brian Dye--Symantec's senior …
strategy for indicators of compromise. This famous quote that Brian Dye--Symantec's senior …
Explainable machine learning for malware detection on android applications
C Palma, A Ferreira, M Figueiredo - Information, 2024 - mdpi.com
The presence of malicious software (malware), for example, in Android applications (apps),
has harmful or irreparable consequences to the user and/or the device. Despite the …
has harmful or irreparable consequences to the user and/or the device. Despite the …
A new tool for static and dynamic Android malware analysis
AndroPyTool is a tool for the extraction of both, static and dynamic features from Android
applications. It aims to provide Android malware analysts with an integrated environment to …
applications. It aims to provide Android malware analysts with an integrated environment to …
[PDF][PDF] Android malware classification based on mobile security framework
In this paper, a machine learning based technique is proposed to classify android
applications in three classes based on the confidence level defined as safe, suspicious and …
applications in three classes based on the confidence level defined as safe, suspicious and …
Getting ahead of the arms race: hothousing the coevolution of virustotal with a packer
Malware detection is in a coevolutionary arms race where the attackers and defenders are
constantly seeking advantage. This arms race is asymmetric: detection is harder and more …
constantly seeking advantage. This arms race is asymmetric: detection is harder and more …