A review of android malware detection approaches based on machine learning
K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are developing rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …
malware is also emerging in an endless stream. Many researchers have studied the …
[HTML][HTML] An in-depth review of machine learning based Android malware detection
It is estimated that around 70% of mobile phone users have an Android device. Due to this
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
popularity, the Android operating system attracts a lot of malware attacks. The sensitive …
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 …
Droidcat: Effective android malware detection and categorization via app-level profiling
Most existing Android malware detection and categorization techniques are static
approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program …
approaches, which suffer from evasion attacks, such as obfuscation. By analyzing program …
Machine learning aided Android malware classification
N Milosevic, A Dehghantanha, KKR Choo - Computers & Electrical …, 2017 - Elsevier
The widespread adoption of Android devices and their capability to access significant
private and confidential information have resulted in these devices being targeted by …
private and confidential information have resulted in these devices being targeted by …
PermPair: Android Malware Detection Using Permission Pairs
The Android smartphones are highly prone to spreading the malware due to intrinsic
feebleness that permits an application to access the internal resources when the user grants …
feebleness that permits an application to access the internal resources when the user grants …
DTMIC: Deep transfer learning for malware image classification
In the ever-changing cyber threat landscape, evolving malware threats demand a new
technique for their detection. This paper puts forward a strategy for distinguishing malware …
technique for their detection. This paper puts forward a strategy for distinguishing malware …
A system call-based android malware detection approach with homogeneous & heterogeneous ensemble machine learning
The enormous popularity of Android in the smartphone market has gained the attention of
malicious actors as well. Also, considering its open system architecture, malicious attacks …
malicious actors as well. Also, considering its open system architecture, malicious attacks …
Droidfusion: A novel multilevel classifier fusion approach for android malware detection
Android malware has continued to grow in volume and complexity posing significant threats
to the security of mobile devices and the services they enable. This has prompted increasing …
to the security of mobile devices and the services they enable. This has prompted increasing …
Multi-view deep learning for zero-day Android malware detection
Zero-day malware samples pose a considerable danger to users as implicitly there are no
documented defences for previously unseen, newly encountered behaviour. Malware …
documented defences for previously unseen, newly encountered behaviour. Malware …