Results 191 to 200 of about 20,533 (244)

Detection of android malware with deep learning method using convolutional neural network model

Reza Maulana, Deris Stiawan, Rahmat Budiarto   +2 more
openalex   +2 more sources

DEFEAT: Android device behavior-based datasets for multi-stage APT. [PDF]

Data Brief
Jabar T, Al-Kadhimi AA, Singh MM.
europepmc   +1 more source

FL-MalDrift: a federated learning framework for malware detection under local concept drift. [PDF]

Sci Rep
Patel A, Tomar DS, Pateriya RK, Haripriya R.   +3 more
europepmc   +1 more source

Mobile malware detection method using improved GhostNetV2 with image enhancement technique. [PDF]

Sci Rep
Du Y, Gao C, Chen X, Cui M, Xu L, Ning A.   +5 more
europepmc   +1 more source

A Method for Real-World Privacy-Preserving Android Malware Detection Through Federated Machine Learning

Giovanni Ciaramella, Fabio Martinelli, Francesco Mercaldo, Christian Peluso, Antonella Santone   +4 more
openalex   +1 more source

A distributed framework for zero-day malware detection using federated ensemble models. [PDF]

PLoS One
Ishfaq H, Shah JH, Saleem R, Afzal M.
europepmc   +1 more source

Improving Android Malware Detection with Convolutional Neural Networks and Long Short-Term Memory

Rafid Sagban, Dr. Rana Hikmet Tobia Saloom, Dr. Naseer Ali Hussien   +2 more
openalex   +1 more source

Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]

Sci Rep
Abd-Ellah MK, Alsayed NA, Elkomy OM, El-Hady WM.   +3 more
europepmc   +1 more source

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Learning Android Malware

Proceedings of the 12th International Conference on Availability, Reliability and Security, 2017
The number of Android malware is increasing every day. Thus Android malware detection is nowadays a big challenge. One of the most tedious tasks in malware detection is the extraction of malicious behaviors. This task is usually done manually and requires a huge effort of engineering. To avoid this step, we propose in this paper to use machine learning
Khanh-Huu-The Dam, Tayssir Touili
openaire   +1 more source

Anatomizing Android Malwares

2019 26th Asia-Pacific Software Engineering Conference (APSEC), 2019
Android OS being the popular choice of majority users also faces the constant risk of breach of confidentiality, integrity and availability (CIA). Effective mitigation efforts needs to identified in order to protect and uphold the CIA triad model, within the android ecosystem.
Anand Tirkey, Ramesh Kumar Mohapatra, Lov Kumar   +2 more
openaire   +1 more source