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010   $a3-030-62582-6
024 7 $a10.1007/978-3-030-62582-5
035   $a(CKB)4100000011675341
035   $a(DE-He213)978-3-030-62582-5
035   $a(MiAaPQ)EBC6432053
035   $a(PPN)252518160
035   $a(EXLCZ)994100000011675341
100   $a20210604d2021      uy             0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aMalware analysis using artificial intelligence and deep learning /$fMark Stamp, Mamoun Alazab, Andrii Shalaginov, editors
205   $a1st ed. 2021.
210  1$aCham, Switzerland :$cSpringer,$d[2021]
210  4$d©2021
215   $a1 online resource (XX, 651 p. 253 illus., 209 illus. in color.)
311   $a3-030-62581-8 
320   $aIncludes bibliographical references.
327   $a1. Optimizing Multi-class Classi?cation 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 Classi?cation -- 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 Pro?les 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 Classi?cation -- 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 Classi?cation 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 Identi?cation and Classi?cation -- 20. Image Spam Classi?cation 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 Arti?cial Intelligence Cyber Threat Assessment Techniques for Increased System Survivability -- 25. Universal Adversarial Perturbations and Image Spam Classi?ers.
330   $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.
606   $aMalware (Computer software)
606   $aArtificial intelligence
606   $aComputer security
606   $aMachine learning
615  0$aMalware (Computer software)
615  0$aArtificial intelligence.
615  0$aComputer security.
615  0$aMachine learning.
676   $a005.84
702   $aAlazab$b Mamoun
702   $aShalaginov$b Andrii
702   $aStamp$b Mark
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bUtOrBLW
906   $aBOOK
912   $a996464411103316
996   $aMalware analysis using artificial intelligence and deep learning$92814723
997   $aUNISA