Android Malware Clustering through Malicious Payload Mining
, 2017 Clustering has been well studied for desktop malware analysis as an effective triage method. Conventional similarity-based clustering techniques, however, cannot be immediately applied to Android malware analysis due to the excessive use of third-party ...
I Santos +7 more
core +1 more source
Analysis of Uapush Malware Infection using Static and Behavior Method on Android [PDF]
, 2018 This research combines static and behavior analysis to detect malwares on Android system. The analysis process was completed by implementing analysis process on a malware-infected application running on an Android device.
Masduqi, Mohammad Khairul +2 more
core +3 more sources
Efficient Deep Learning Network With Multi-Streams for Android Malware Family Classification
IEEE Access, 2022 It is important to effectively detect, mitigate, and defend against Android malware attacks, because Android malware has long represented a major threat to Android app security.
Hyun-Il Kim +3 more
doaj +1 more source
Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications [PDF]
, 2018 Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces.
Argyriou, Marios +2 more
core +2 more sources
Testing Android Anti-Malware against Malware Obfuscations
International Journal of Computer Applications, 2015 is an increasing threat of malware on mobile. Since Android is the most popular and maximum sold mobile phone, the malware attack on Android mobile is increasing day by day. The commercial antimalware products available in the market can detect common and old malwares easily.
Aruna Gupta, Gunjan Kapse
openaire +1 more source
Longitudinal performance analysis of machine learning based Android malware detectors [PDF]
, 2019 This paper presents a longitudinal study of the performance of machine learning classifiers for Android malware detection. The study is undertaken using features extracted from Android applications first seen between 2012 and 2016.
Khan, Sarmadullah, Yerima, Suleiman
core +1 more source
Security Toolbox for Detecting Novel and Sophisticated Android Malware
, 2015 This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project.
Deering, Tom +4 more
core +1 more source
DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection
Applied Sciences, 2023 Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher +4 more
doaj +1 more source
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
, 2018 The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction of features fits well in response to the rapid ...
Huang, TonTon Hsien-De, Kao, Hung-Yu
core +1 more source
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild [PDF]
, 2018 In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Chen, Kai +9 more
core +2 more sources