Results 191 to 200 of about 20,046 (244)

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

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

Advance Android PHAs/Malware Detection Techniques by Utilizing Signature Data, Behavioral Patterns and Machine Learning

, 2016
Suyash Jadhav
openalex   +1 more source

Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms. [PDF]

Sci Rep
Alohali MA, Alahmari S, Aljebreen M, Asiri MM, Miled AB, Albouq SS, Alrusaini O, Alqazzaz A.   +7 more
europepmc   +1 more source

An accurate approach to discriminate android colluded malware from single app malware using permissions intelligence. [PDF]

Sci Rep
Mawoh RY, Wacka JBA, Tchakounte F, Fachkha C, Kolyang.   +4 more
europepmc   +1 more source

MALITE: Lightweight Malware Detection and Classification for Constrained Devices. [PDF]

IEEE Trans Emerg Top Comput
Anand S, Mitra B, Dey S, Rao A, Dhar R, Vaidya J.   +5 more
europepmc   +1 more source

Multimodal malware classification using proposed ensemble deep neural network framework. [PDF]

Sci Rep
Nazim S, Alam MM, Rizvi S, Mustapha JC, Hussain SS, Su'ud MM.   +5 more
europepmc   +1 more source

Data trace as the scientific foundation for trusted metrological data: a review for future metrology direction. [PDF]

PeerJ Comput Sci
Cao Z, Gao B, Liu Z, Xiong X, Wang B, Pei C.   +5 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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