Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Junyang Qiu"'
Publikováno v:
IEEE Access, Vol 7, Pp 66304-66316 (2019)
Recently, Android malicious samples threaten billions of mobile end users' security or privacy. The community researchers have designed many methods to automatically and accurately identify Android malware samples. However, the rapid increase of Andr
Externí odkaz:
https://doaj.org/article/eef1c24ec36140de8ad4b7f9542959af
Publikováno v:
IEEE Access, Vol 7, Pp 140488-140498 (2019)
With the rapid progress of urbanization, predicting citywide crowd flows has become increasingly significant in many fields, such as traffic management and public security. However, influenced by the complex spatiotemporal relations in raw data and o
Externí odkaz:
https://doaj.org/article/234d08d6146440c681266db079314000
Publikováno v:
IEEE Access, Vol 7, Pp 147156-147168 (2019)
Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of
Externí odkaz:
https://doaj.org/article/b3b1082a18284bbc87d1555a8f19b9ec
Publikováno v:
IEEE Access, Vol 7, Pp 155270-155280 (2019)
Malware and its variants continue to pose a threat to network security. Machine learning has been widely used in the field of malware classification, but some emerging studies, such as attention mechanisms, are rarely applied in this field. In this p
Externí odkaz:
https://doaj.org/article/d9b7775571954589a8d6243939f6e27c
Publikováno v:
IEEE Transactions on Cybernetics. 53:617-627
Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families' malware may remain undetected until the classif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ec1defeb46ee2b94e795974686c7874
https://doi.org/10.1109/tcyb.2022.3164625
https://doi.org/10.1109/tcyb.2022.3164625
Autor:
JUNYANG QIU1, JUN ZHANG2, WEI LUO1, LEI PAN1, NEPAL, SURYA3 Surya.Nepal@data61.csiro.au, YANG XIANG2
Publikováno v:
ACM Computing Surveys. Nov2021, Vol. 53 Issue 6, p1-36. 36p.
Publikováno v:
Neurocomputing. 468:198-210
Network embedding aims to learn a low-dimensional vector for each node in networks, which is effective in a variety of applications such as network reconstruction and community detection. However, the majority of the existing network embedding method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::498e22201f547fcc907e5f075653af2e
https://doi.org/10.1016/j.neucom.2021.10.032
https://doi.org/10.1016/j.neucom.2021.10.032
Publikováno v:
ACM Computing Surveys. 53:1-36
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber security research. Deep learning models have many advantages over traditional Machine Learning (ML) models, particularly when there is a large amount of data availa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3db62802fdbec37d1ef04d4ae5ca347b
https://doi.org/10.1145/3417978
https://doi.org/10.1145/3417978
Publikováno v:
Applied Intelligence. 50:3976-3989
Network embedding is an effective method aiming to learn the low-dimensional vector representation of nodes in networks, which has been widely used in various network analytic tasks such as node classification, node clustering, and link prediction. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8124b75d394c7562f5f778c951e0f4a9
https://doi.org/10.1007/s10489-020-01780-7
https://doi.org/10.1007/s10489-020-01780-7
Publikováno v:
International Journal of Computers and Applications. 44:338-348
The popularity of social media networks, such as Twitter, leads to an increasing number of spamming activities. Researchers employed various machine learning methods to detect Twitter spam. However...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c15f8f200f9e37739ff217c821e38d93
https://doi.org/10.1080/1206212x.2020.1751387
https://doi.org/10.1080/1206212x.2020.1751387