| 000 | 04878nam a22005775i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-62582-5 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125347.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 201220s2021 sz | s |||| 0|eng d | ||
| 020 |
_a9783030625825 _9978-3-030-62582-5 |
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| 024 | 7 |
_a10.1007/978-3-030-62582-5 _2doi |
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| 245 | 1 | 0 |
_aMalware Analysis Using Artificial Intelligence and Deep Learning _h[electronic resource] / _cedited by Mark Stamp, Mamoun Alazab, Andrii Shalaginov. |
| 250 | _a1st ed. 2021. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
|
| 300 |
_aXX, 651 p. 253 illus., 209 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_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 | _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. | ||
| 650 | 0 | _aComputer crimes. | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aComputational intelligence. | |
| 650 | 0 | _aData protection. | |
| 650 | 1 | 4 | _aComputer Crime. |
| 650 | 2 | 4 | _aMachine Learning. |
| 650 | 2 | 4 | _aComputational Intelligence. |
| 650 | 2 | 4 | _aSecurity Services. |
| 700 | 1 |
_aStamp, Mark. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aAlazab, Mamoun. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 700 | 1 |
_aShalaginov, Andrii. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030625818 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030625832 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030625849 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-62582-5 |
| 912 | _aZDB-2-SCS | ||
| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
| 999 |
_c177100 _d177100 |
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