| ISBN |
9783030625825 |
| 기타 표준번호 |
10.1007/978-3-030-62582-5 |
| 청구기호 |
HV6772-6773.3 |
| 형태사항 |
XX, 651 p. 253 illus., 209 illus. in color. online resource.
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| 언어 |
English |
| 내용 |
1. 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.
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| 주제 |
Computer crimes.
Machine learning.
Computational intelligence.
Data protection.
Computer Crime.
Machine Learning.
Computational Intelligence.
Security Services.
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| 보유판 및 특별호 저록 |
Springer Nature eBook
Printed edition: 9783030625818
Printed edition: 9783030625832
Printed edition: 9783030625849
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| QR CODE |
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