Measuring and modeling the label dynamics of online {Anti-Malware} engines

S Zhu, J Shi, L Yang, B Qin, Z Zhang, L Song… - 29th USENIX Security …, 2020 - usenix.org
VirusTotal provides malware labels from a large set of anti-malware engines, and is heavily
used by researchers for malware annotation and system evaluation. Since different engines …

A survey of recent advances in deep learning models for detecting malware in desktop and mobile platforms

P Maniriho, AN Mahmood, MJM Chowdhury - ACM Computing Surveys, 2024 - dl.acm.org
Malware is one of the most common and severe cyber threats today. Malware infects
millions of devices and can perform several malicious activities including compromising …

Analysis of android malware detection techniques: a systematic review

MA Ashawa, S Morris - 2019 - dspace.lib.cranfield.ac.uk
The emergence and rapid development in complexity and popularity of Android mobile
phones has created proportionate destructive effects from the world of cyber-attack. Android …

Android malware detection based on factorization machine

C Li, K Mills, D Niu, R Zhu, H Zhang, H Kinawi - IEEE Access, 2019 - ieeexplore.ieee.org
As the popularity of Android smart phones has increased in recent years, so too has the
number of malicious applications. Due to the potential for data theft that mobile phone users …

Android malware detection methods based on convolutional neural network: A survey

L Shu, S Dong, H Su, J Huang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Android malware detection (AMD) is a challenging task requiring many factors to be
considered during detection, such as feature extraction and processing, performance …

Graph convolutional networks for android malware detection with system call graphs

TS John, T Thomas, S Emmanuel - 2020 Third ISEA …, 2020 - ieeexplore.ieee.org
Nowadays, Android malwares have risen precipitously causing critical security threats.
Malware authors now employ a variety of obfuscation techniques to evade their detection …

[HTML][HTML] WebAssembly diversification for malware evasion

J Cabrera-Arteaga, M Monperrus, T Toady… - Computers & Security, 2023 - Elsevier
WebAssembly has become a crucial part of the modern web, offering a faster alternative to
JavaScript in browsers. While boosting rich applications in browser, this technology is also …

Obfuscation detection in android applications using deep learning

M Conti, P Vinod, A Vitella - Journal of Information Security and …, 2022 - Elsevier
Malware is often hidden in illegitimately cloned software. Android, with over two billions
active devices, is one of the most affected platforms because code cloning is quite simple …

Task-Aware Meta Learning-Based Siamese Neural Network for Classifying Control Flow Obfuscated Malware

J Zhu, J Jang-Jaccard, A Singh, PA Watters… - Future Internet, 2023 - mdpi.com
Malware authors apply different techniques of control flow obfuscation, in order to create
new malware variants to avoid detection. Existing Siamese neural network (SNN)-based …

A malware evasion technique for auditing android anti-malware solutions

S Mirza, H Abbas, WB Shahid, N Shafqat… - 2021 IEEE 30th …, 2021 - ieeexplore.ieee.org
In the past few years, Android security is enhanced and state-of-the-art anti-malware tools
have been introduced to counter Android malware. These tools use both static and dynamic …