Titlebook: Cyberspace Safety and Security; 12th International S Jieren Cheng,Xiangyan Tang,Xiaozhang Liu Conference proceedings 2021 Springer Nature S

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書(shū)目名稱(chēng)Cyberspace Safety and Security影響因子(影響力)




書(shū)目名稱(chēng)Cyberspace Safety and Security影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Cyberspace Safety and Security網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Cyberspace Safety and Security網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Cyberspace Safety and Security被引頻次




書(shū)目名稱(chēng)Cyberspace Safety and Security被引頻次學(xué)科排名




書(shū)目名稱(chēng)Cyberspace Safety and Security年度引用




書(shū)目名稱(chēng)Cyberspace Safety and Security年度引用學(xué)科排名




書(shū)目名稱(chēng)Cyberspace Safety and Security讀者反饋




書(shū)目名稱(chēng)Cyberspace Safety and Security讀者反饋學(xué)科排名

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Indizieren und Gliedern von Dokumenten,ability, and the artificial designed adversarial examples can make the DNN model output the wrong results. These adversarial examples not only exist in the digital world, but also in the physical world. At present, researches on autonomous driving platform mainly focus on attacking a single sensor.

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https://doi.org/10.1007/978-1-4302-7214-4vulnerability of malware detector and improve the defense ability of cyberspace. Considering the huge market share of android system, adversarial malware examples of android are studied in this paper. And an algorithm is proposed to find universal adversarial perturbations of malware. Such perturbat

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https://doi.org/10.1007/978-1-4302-7214-4includes a random projection method for reducing feature dimensionality which would be more efficient than usual feature selection methods in existing work for the task. We also introduced a new method of SGD-based SVM with adapted sampling, which was based on the insight from the confidence and nea

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https://doi.org/10.1007/978-1-4302-7214-4h field of adversarial machine learning. Support vector machines (SVMs), as a kind of successful approach, were widely used to solve security problems, such as image classification, malware detection, spam filtering, and intrusion detection. However, many adversarial attack methods have emerged rece

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https://doi.org/10.1007/978-1-4842-2439-7ts. Different attack and defense strategies have been proposed to better study the security of deep neural networks. But these works only focus on an aspect such as attack or defense. In this work, we propose a robust GAN based on the attention mechanism, which uses the deep latent features of the o