A survey of android malware detection with deep neural models
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber
security research. Deep learning models have many advantages over traditional Machine …
security research. Deep learning models have many advantages over traditional Machine …
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 …
Effective and efficient hybrid android malware classification using pseudo-label stacked auto-encoder
Android has become the target of attackers because of its popularity. The detection of
Android mobile malware has become increasingly important due to its significant threat …
Android mobile malware has become increasingly important due to its significant threat …
Dynamic android malware category classification using semi-supervised deep learning
Due to the significant threat of Android mobile malware, its detection has become
increasingly important. Despite the academic and industrial attempts, devising a robust and …
increasingly important. Despite the academic and industrial attempts, devising a robust and …
GDroid: Android malware detection and classification with graph convolutional network
The dramatic increase in the number of malware poses a serious challenge to the Android
platform and makes it difficult for malware analysis. In this paper, we propose a novel …
platform and makes it difficult for malware analysis. In this paper, we propose a novel …
[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 …
Similarity-based Android malware detection using Hamming distance of static binary features
In this paper, we develop four malware detection methods using Hamming distance to find
similarity between samples which are first nearest neighbors (FNN), all nearest neighbors …
similarity between samples which are first nearest neighbors (FNN), all nearest neighbors …
Mobile malware attacks: Review, taxonomy & future directions
A pervasive increase in the adoption rate of smartphones with Android OS is noted in recent
years. Android's popular and attractive environment not only captured the attention of users …
years. Android's popular and attractive environment not only captured the attention of users …
[HTML][HTML] Kronodroid: time-based hybrid-featured dataset for effective android malware detection and characterization
Android malware evolution has been neglected by the available data sets, thus providing a
static snapshot of a non-stationary phenomenon. The impact of the time variable has not had …
static snapshot of a non-stationary phenomenon. The impact of the time variable has not had …
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 …