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Titlebook: Information Security Applications; 21st International C Ilsun You Conference proceedings 2020 Springer Nature Switzerland AG 2020 Computer

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樓主
發(fā)表于 2025-3-21 16:53:28 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Information Security Applications
副標(biāo)題21st International C
編輯Ilsun You
視頻videohttp://file.papertrans.cn/466/465300/465300.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Information Security Applications; 21st International C Ilsun You Conference proceedings 2020 Springer Nature Switzerland AG 2020 Computer
描述.This book constitutes the thoroughly refereed proceedings of the 21st International Conference on Information Security Applications, WISA 2020, held in Jeju Island, South Korea, in August 2020...The 30 full research papers included in this book were carefully reviewed and selected from 89 submissions. They are organized in the following topical sections: AI Security and Intrusion Detection; Steganography and Malware; Application, System, and Hardware Security; Cryptography; Advances in Network Security and Attack Defense; and Cyber Security..
出版日期Conference proceedings 2020
關(guān)鍵詞Computer Science; artificial intelligence; security services; software and application security; network
版次1
doihttps://doi.org/10.1007/978-3-030-65299-9
isbn_softcover978-3-030-65298-2
isbn_ebook978-3-030-65299-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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板凳
發(fā)表于 2025-3-22 01:24:03 | 只看該作者
Spatially Localized Perturbation GAN (SLP-GAN) for Generating Invisible Adversarial Patchesattention map) as the adversarial patches. The patch region is extracted using the Grad-CAM algorithm to improve the attacking ability against the target model. Our experiment, tested on GTSRB and CIFAR-10 datasets, shows that SLP-GAN outperforms the state-of-the-art adversarial patch attack methods in terms of visual fidelity.
地板
發(fā)表于 2025-3-22 06:59:36 | 只看該作者
Entropy-Based Feature Grouping in Machine Learning for Android Malware Classificationfication using machine learning, 14,290 APKs from 14 different categories are collected and trained with 10,003 APKs and tested with 4,287 APKs among them. As a result, we got 98.83% of true positive rate and 1.16% of false positive rate on average, with 98.8% of F-measure score.
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發(fā)表于 2025-3-22 17:40:39 | 只看該作者
On the Cost-Effectiveness of TrustZone Defense on ARM Platform Cortex-M series chips. Our experimental evaluation and theoretical analysis show the effectiveness and efficiency of FLUSH operations when entering and leaving TrustZone, which helps in design defense framework based on our research.
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發(fā)表于 2025-3-22 22:20:01 | 只看該作者
CAPTCHAs Are Still in Danger: An Efficient Scheme to Bypass Adversarial CAPTCHAsriodically retrain the solver model when its accuracy drops. The proposed method uses incremental learning that requires a small amount of data while achieving high accuracy. We demonstrate that the proposed solver bypasses the existing defense methods based on a text-based CAPTCHA scheme and an image-based CAPTCHA scheme.
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發(fā)表于 2025-3-23 02:36:23 | 只看該作者
Conference proceedings 2020in Jeju Island, South Korea, in August 2020...The 30 full research papers included in this book were carefully reviewed and selected from 89 submissions. They are organized in the following topical sections: AI Security and Intrusion Detection; Steganography and Malware; Application, System, and Har
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