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_a10.1007/978-3-030-62582-5 _2doi |
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_aQA76.76.C68 _b2021 EB |
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_aMalware Analysis Using Artificial Intelligence and Deep Learning _cedited by Mark Stamp, Mamoun Alazab, Andrii Shalaginov |
| 250 | _aFirst edition 2021 | ||
| 264 | 1 |
_aCham, Switzerland _bSpringer International Publising _c2021 |
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_a1 recurso en línea (XX, 651 páginas) _b253 ilustraciones, 209 ilustraciones a color |
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_2rdacontent _aTexto _btxt |
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_2rdamedia _aelectrónico _bc |
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_2rdacarrier _arecurso electrónico _bcr |
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_atext file _bPDF _2rda |
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| 505 | 0 | _a1. Optimizing Multi-class Classification of Binaries Based on Static Features -- 2.Detecting Abusive Comments Using Ensemble Deep Learning Algorithms -- 3. Deep Learning Techniques for Behavioural Malware Analysis in Cloud IaaS -- 4. Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges -- 5. A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysis -- 6. A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification -- 7. Word Embedding Techniques for Malware Evolution Detection -- 8. Reanimating Historic Malware Samples -- 9. DURLD: Malicious URL detection using Deep learning based Character-level representations -- 10. Sentiment Analysis for Troll Detection on Weibo -- 11. Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families -- 12. Review of the Malware Categorization in the Era of Changing Cybethreats Landscape: Common Approaches, Challenges and Future Needs -- 13. An Empirical Analysis of Image-Based Learning Techniques for Malware Classification -- 14. A Survey of Intelligent Techniques for Android Malware Detection -- 15. Malware Detection with Sequence-Based Machine Learning and Deep Learning -- 16. A Novel Study on Multinomial Classification of x86/x64 Linux ELF Malware Types and Families through Deep Neural Networks -- 17. Cluster Analysis of Malware Family Relationships -- 18. Log-Based Malicious Activity Detection using Machine and Deep Learning -- 19. Deep Learning in Malware Identification and Classification -- 20. Image Spam Classification with Deep Neural Networks -- 21. Fast and Straightforward Feature Selection Method -- 22. On Ensemble Learning -- 23. A Comparative Study of Adversarial Attacks to Malware Detectors Based on Deep Learning -- 24. Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivability -- 25. Universal Adversarial Perturbations and Image Spam Classifiers. | |
| 520 | 3 | _aThis book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases. | |
| 988 | _aSpringer_Computer_2021 | ||
| 650 | 7 |
_2embne _9678568 _aMalware (Programas de ordenador) |
|
| 650 | 7 |
_2embne _aSeguridad informática _9158200 |
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| 700 | 1 |
_aStamp, Mark _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aAlazab, Mamoun _eeditor literario _0(orcid)0000-0002-1928-3704 _1https://orcid.org/0000-0002-1928-3704 _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aShalaginov, Andrii _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030625818 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030625832 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030625849 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-62582-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b05/2021 _dz _ek _zSI |
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