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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
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
Eight years of rider measurement in the Android malware ecosystem: evolution and lessons learned [PDF]
, 2018 Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform.
Stringhini, Gianluca +1 more
core
Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation [PDF]
, 2013 We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control ...
Aldous, Petey +6 more
core +2 more sources
Forensic Analysis of Hook Android Malware
Forensic Science International: Digital Investigation, 2023 This publication presents a thorough forensic investigation of the banking malware known as Hook, shedding light on its intricate functionalities and providing valuable insights into the broader realm of banking malware. Given the persistent evolution of Android malware, particularly in the context of banking threats, this research explores the ongoing
Dominic Schmutz +2 more
openaire +1 more source
Mission Aware Cyber‐Physical Security
Systems Engineering, Volume 29, Issue 2, Page 354-367, March 2026.ABSTRACT Perimeter cybersecurity, while essential, has proven insufficient against sophisticated, coordinated, and cyber‐physical attacks. In contrast, mission‐centric cybersecurity emphasizes finding evidence of attack impact on mission success, allowing for targeted resource allocation to mitigate vulnerabilities and protect critical assets.
Georgios Bakirtzis +3 more
wiley +1 more source
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
, 2018 Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary.
Chen, Xiao +7 more
core +1 more source
Explaining Black-box Android Malware Detection [PDF]
2018 26th European Signal Processing Conference (EUSIPCO), 2018 Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions.
Marco Melis +4 more
openaire +4 more sources
Expert Systems, Volume 43, Issue 3, March 2026.ABSTRACT Zero‐day exploits remain challenging to detect because they often appear in unknown distributions of signatures and rules. The article entails a systematic review and cross‐sectional synthesis of four fundamental model families for identifying zero‐day intrusions, namely, convolutional neural networks (CNN), deep neural networks (DNN ...
Abdullah Al Siam +3 more
wiley +1 more source
IEEE AccessAndroid malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets.
Mohammed Maray +5 more
doaj +1 more source