Results 111 to 120 of about 2,904 (224)

Concept Drift Detection in Android Malware

, 2023
Machine learning and deep learning algorithms have been successfully applied to the problems of malware detection, classification, and analysis. However, most of such studies have been limited to applying learning algorithms to a static snapshot of ...
Singh, Inderpreet
core   +1 more source

A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

IEEE Access, 2019
Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware.
Zhuo Ma, Haoran Ge, Yang Liu, Meng Zhao, Jianfeng Ma   +4 more
doaj   +1 more source

Android Malware Detection Using Backpropagation Neural Network

, 2016
The rapid growing adoption of android operating system around the world affects the growth of malware that attacks this platform. One possible solution to overcome the threat of malware is building a comprehensive system to detect existing malware.
Herman Tolle, Tolle, Herman, Fais Al Huda, Firdaus Mahmudy, Wayan, Al Huda, Fais, Wayan Firdaus Mahmudy   +5 more
core   +1 more source

Android malware detection with unbiased confidence guarantees

Neurocomputing, 2018
The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, without
Harris Papadopoulos, Nestoras Georgiou, Charalambos Eliades, Andreas Konstantinidis 0002   +3 more
openaire   +2 more sources

Android Malware Detection Based on Factorization Machine

IEEE Access, 2019
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 mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security.
Chenglin Li, Keith G. Mills, Di Niu, Rui Zhu 0007, Hongwen Zhang 0002, Husam Kinawi   +5 more
openaire   +3 more sources

Large scale android malware detection

, 2018
Smartphones’ popularity and use has been increasing exponentially over the years. This also opens up the chance of damage to be done by malicious software or malware for short.
Kasim, Arief Kresnadi Ignatius
core  

AAGAN: Android Malware Generation System Based on Generative Adversarial Network

Vietnam Journal of Computer Science
With the rapid evolution of mobile malware, especially Android malware, machine learning (ML)-based Android malware detection systems have drawn massive attention. Although ML algorithms have recently led to many vital breakthroughs in malware detection,
Doan Minh Trung, Nghi Hoang Khoa, Phan The Duy, Van-Hau Pham, Nguyen Tan Cam   +4 more
doaj   +1 more source

Android Malware Detection via Graphlet Sampling

, 2018
Android systems are widely used in mobile & wireless distributed systems. In the near future, Android is believed to dominate the mobile distributed environment.
Sisodia, Devkishen, Li, Feng, Gao, Tianchong, Saha, Tanay Kumar, Al Hasan, Mohammad, Peng, Wei   +5 more
core   +1 more source

Semi-Supervised Models for Android Malware Detection

, 2021
This thesis focuses on the detection of Android malware. It proposes detection models to detect malicious applications. It introduces Semi-supervised detection models and achieves superior performance.
Shaila Sharmeen (13513624)
core  

Android malware detection with MH-100K: An innovative dataset for advanced research. [PDF]

Data Brief, 2023
Bragança H, Rocha V, Barcellos L, Souto E, Kreutz D, Feitosa E.   +5 more
europepmc   +1 more source