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Malware Detection in Android Applications

, 2019
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016.
Mr. Tushar Patil, Prof. Bharti Dhote
openaire   +1 more source

Feature Graph Construction With Static Features for Malware Detection

IET Information Security, Volume 2025, Issue 1, 2025.
Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion‐based detection methods generally overlook the correlation between features. And mere concatenation of features will reduce the model’s characterization ability, lead to low detection accuracy. Moreover, these
Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun, Stelvio Cimato   +7 more
wiley   +1 more source

WinDroid: A Novel Framework for Windows and Android Malware Family Classification Using Hierarchical Ensemble Support Vector Machines With Multiview Handcrafted and Deep Learning Features

IET Information Security, Volume 2025, Issue 1, 2025.
The rapid growth and diversification of malware variants, driven by advanced code obfuscation, evasion, and antianalysis techniques, present a significant threat to cybersecurity. The inadequacy of traditional methods in accurately classifying these evolving threats highlights the need for effective and robust malware classification techniques.
K. Sundara Krishnan, S. Syed Suhaila, Hyun-A Park   +2 more
wiley   +1 more source

MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection

IET Software, Volume 2025, Issue 1, 2025.
Nowadays, the Android system is widely used in mobile devices. The existence of malware in the Android system has posed serious security risks. Therefore, detecting malware has become a main research focus for Android devices. The existing malware detection methods include those based on static analysis, dynamic analysis, and hybrid analysis.
Bo Yang, Mengbo Li, Li Li, Huai Liu, Nadeem Sarwar   +4 more
wiley   +1 more source

Android Malware Detection Systems Review

Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2017
With the smartphones entering our lives, the number of smartphones continues to increase day by day. The reason why smartphones are in so demand is that people can easily do what they want. According to IDC's 2016 Q2 report, Android dominated the smartphone market with an 87.6% share [1].
Ömer Kiraz, İbrahim Alper Doğru
openaire   +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
Papadopoulos, Harris, Georgiou, Nestoras, Eliades, Charalambos, Konstantinidis, Andreas   +3 more
openaire   +2 more sources

Android Malware Detection using ML

International Journal of Advanced Research in Science, Communication and Technology
Android devices are more prone of malware attacks due to its open-source nature. This makes it easier for installing applications from various sources, which can lead to major security issues. Machine learning learns from examples. It studies data from apps both good and bad and understands its characteristics.
null Pradhipa S, null Poornima, null Indu A, null Reena K   +3 more
openaire   +1 more source

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 Mills, Di Niu, Rui Zhu, Hongwen Zhang, Husam Kinawi   +5 more
openaire   +3 more sources

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

An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs. [PDF]

Sensors (Basel), 2023
Wu H, Luktarhan N, Tian G, Song Y.
europepmc   +1 more source