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MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection

IET Software
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.
Bo Yang, Mengbo Li, Li Li, Huai Liu
doaj   +1 more source

Machine learning methods for Android malware detection

, 2015
With the Android mobile device becoming increasingly popular, the Android application market has become a main target of the malware attacks. Therefore, many methods have been used to protect the mobile application users from being attacked.
Xu, Zhengzi
core  

Earthworm optimization algorithm based cascade LSTM-GRU model for android malware detection

Cyber Security and Applications
The rise in mobile malware risks brought on by the explosion of Android smartphones required more efficient detection techniques. Inspired by a cascade of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, optimized using the ...
Brij B. Gupta, Akshat Gaurav, Varsha Arya, Shavi Bansal, Razaz Waheeb Attar, Ahmed Alhomoud, Konstantinos Psannis   +6 more
doaj   +1 more source

DL-AMDet: Deep learning-based malware detector for android

Intelligent Systems with Applications
The Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of
Ahmed R. Nasser, Ahmed M. Hasan, Amjad J. Humaidi   +2 more
doaj   +1 more source

Chat-GPT for Android malware detection

The use of large-language models (LLMs) in the field of cybersecurity has been increasing greatly in recent years. With the advent of ChatGPT by OpenAI, there have been many different use cases for LLMs in cybersecurity, including in intrusion detection,
Ong, Eliezer De Zhi
core  

Malware Detection in Android Platforms

, 2015
As one of the major operating systems adopted by mobile devices, security issues related to Android platform is gaining increasing attention in the research literature in recent years.
Liu, Peixiang, Li, Wei, Zhang, Yi
core  

Is Malware Detection Needed for Android TV?

The smart TV ecosystem is rapidly expanding, allowing developers to publish their applications on TV markets to provide a wide array of services to TV users.
Mehmet Ali Erturk, Erturk, Mehmet Ali, Gokhan Ozogur, Gurkas-Aydin, Zeynep, Zeynep Gurkas-Aydin, Ozogur, Gokhan   +5 more
core   +1 more source

Android Malware Detection using Deep Learning Classification Approach

Android devices are becoming increasingly popular, and there are more threats to Android users. This paper discusses Android malware detection using a deep learning classification approach.
Nik Zulkipli, Nurul Huda, Razak, Tajul Rosli, Mohd Fuzi, Mohd Faris, Amri, Nur Amirah, Ismail, Mohammad Hafiz, Darus, Mohamad Yusof   +5 more
core   +1 more source

Image-Based Android Malware Detection Using Deep Learning

The Android operating system (OS) dominates the mobile phone OS industry, with over 70% of the market share. With the growth of Android OS-based smartphones, it has become a prime target for mobile malware attacks.
USMAN, MUHAMMAD; id_orcid, Jeremiah, Daniel, Udeze, Somadina, TA, VINH-THONG; id_orcid, RAFIQ, HUSNAIN; id_orcid   +4 more
core   +1 more source

HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]

PLoS One
Minh Manh V, Do Xuan C, Van NTK.
europepmc   +1 more source