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Generative malware outbreak detection /
"Recently, several deep learning approaches have attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they've shown respectable performance on a large corpus of datasets, practical defense systems require precise detection during...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly Media,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Park, Sean, |e on-screen presenter. | |
| 245 | 1 | 0 | |a Generative malware outbreak detection / |c Sean Park. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly Media, |c 2020. | |
| 300 | |a 1 online resource (1 streaming video file (46 min., 50 sec.)) | ||
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| 500 | |a Title from resource description page (viewed July 22, 2020). | ||
| 511 | 0 | |a Presenter, Sean Park. | |
| 520 | |a "Recently, several deep learning approaches have attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they've shown respectable performance on a large corpus of datasets, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available. Sean Park (Trend Micro) demonstrates the effectiveness of the latent representations obtained through the adversarial autoencoder for malware outbreak detection. Using instruction sequence distribution mapped to a semantic latent vector, the model provides a highly effective neural signature that helps detecting variants of a previously identified malware within a campaign mutated with minor functional upgrade, function shuffling, or slightly modified obfuscations. Sean explains the effectiveness of generative adversarial autoencoders for static malware detection under outbreak situations where a single sample of a kind is available to detect similar in-the-wild samples. The model performance is evaluated over real-world macOS and Windows malware samples against traditional machine learning models."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer security. | |
| 650 | 0 | |a Malware (Computer software) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Sécurité informatique. | |
| 650 | 6 | |a Logiciels malveillants. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
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| 650 | 7 | |a Malware (Computer software) |2 fast |0 (OCoLC)fst01748230 | |
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| 856 | 4 | 0 | |u https://learning.oreilly.com/videos/~/0636920373452/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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