Zobrazeno 1 - 10
of 175
pro vyhledávání: '"SEIICHI OZAWA"'
Autor:
Muhammad Fakhrur Rozi, Tao Ban, Seiichi Ozawa, Akira Yamada, Takeshi Takahashi, Daisuke Inoue
Publikováno v:
IEEE Access, Vol 12, Pp 142101-142126 (2024)
Detecting source code vulnerabilities is a critical challenge in secure software development. Early identification of vulnerabilities ensures that software performance and security remain uncompromised. However, existing vulnerability detection metho
Externí odkaz:
https://doaj.org/article/de4571ff872e4b52a12c64b3f66cf733
Autor:
Muhammad Fakhrur Rozi, Tao Ban, Seiichi Ozawa, Akira Yamada, Takeshi Takahashi, Sangwook Kim, Daisuke Inoue
Publikováno v:
IEEE Access, Vol 11, Pp 102727-102745 (2023)
Malicious JavaScript code in web applications poses a significant threat as cyber attackers exploit it to perform various malicious activities. Detecting these malicious scripts is challenging, given their diverse nature and the continuous evolution
Externí odkaz:
https://doaj.org/article/96b921f1abd0426fa01fe22f24802029
Publikováno v:
IEEE Access, Vol 10, Pp 43954-43963 (2022)
Privacy protection has attracted increasing attention, and privacy concerns often prevent flexible data utilization. In most industries, data are distributed across multiple organizations due to privacy concerns. Federated learning (FL), which enable
Externí odkaz:
https://doaj.org/article/5b32fea6b6c44c8c984bd3a1715b97d6
Autor:
Parichehr Behjati, Pau Rodriguez, Carles Fernandez Tena, Armin Mehri, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzalez
Publikováno v:
IEEE Access, Vol 10, Pp 57383-57397 (2022)
Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core pro
Externí odkaz:
https://doaj.org/article/eee36a27d907449f80b965886182366b
Autor:
Diego A. Velazquez, Josep M. Gonfaus, Pau Rodriguez, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzalez
Publikováno v:
IEEE Access, Vol 9, Pp 106998-107011 (2021)
In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-
Externí odkaz:
https://doaj.org/article/06053aaa1432435eaf1b7fc745bef20f
Autor:
Muhammad Fakhrur Rozi, Seiichi Ozawa, Tao Ban, Sangwook Kim, Takeshi Takahashi, Daisuke Inoue
Publikováno v:
Applied Sciences, Vol 12, Iss 24, p 12916 (2022)
JavaScript-based attacks injected into a webpage to perpetrate malicious activities are still the main problem in web security. Recent works have leveraged advances in artificial intelligence by considering many feature representations to improve the
Externí odkaz:
https://doaj.org/article/a0512f1e30eb4252802dc67841ed0551
Publikováno v:
CAAI Transactions on Intelligence Technology (2020)
Obfuscation is rampant in both benign and malicious JavaScript (JS) codes. It generates an obscure and undetectable code that hinders comprehension and analysis. Therefore, accurate detection of JS codes that masquerade as innocuous scripts is vital.
Externí odkaz:
https://doaj.org/article/1ec26bbd25b54437bb5faa62e4cf5359
Publikováno v:
Applied Sciences, Vol 12, Iss 1, p 60 (2021)
Attacks using Uniform Resource Locators (URLs) and their JavaScript (JS) code content to perpetrate malicious activities on the Internet are rampant and continuously evolving. Methods such as blocklisting, client honeypots, domain reputation inspecti
Externí odkaz:
https://doaj.org/article/12102e964945465dbdfc67f190972b27
Autor:
Pau Rodríguez, Diego Velazquez, Guillem Cucurull, Josep M. Gonfaus, F. Xavier Roca, Seiichi Ozawa, Jordi Gonzàlez
Publikováno v:
Applied Sciences, Vol 10, Iss 22, p 8170 (2020)
Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increas
Externí odkaz:
https://doaj.org/article/5fb24e5cf26d4375ae81da6db5cc94fc
Publikováno v:
IEEE Access. 10:43954-43963
Privacy protection has attracted increasing attention, and privacy concerns often prevent flexible data utilization. In most industries, data are distributed across multiple organizations due to privacy concerns. Federated learning (FL), which enable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6642da979514fb2cf48f6037ea75a78d
https://hdl.handle.net/20.500.14094/90009239
https://hdl.handle.net/20.500.14094/90009239