LEADER 02205oam 2200625I 450 001 9910704648303321 005 20190510111807.0 035 $a(CKB)5470000002443232 035 $a(OCoLC)1005139607 035 $a(OCoLC)995470000002443232 035 $a(EXLCZ)995470000002443232 100 $a20171003d2012 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Shoreline Management Tool $ean ArcMap tool for analyzing water depth, inundated area, volume, and selected habitats, with an example for the lower Wood River Valley, Oregon /$fby Daniel T. Snyder, Tana L. Haluska, and Darius Respini-Irwin 210 1$aReston, Virginia :$cU.S. Department of the Interior, U.S. Geological Survey,$d2012. 215 $a1 online resource (vi, 86 pages) $cillustrations, maps 225 1 $aOpen-file report ;$v2012-1247 300 $a"Prepared in cooperation with the Bureau of Land Management." 320 $aIncludes bibliographical references (pages 9-10). 517 $aShoreline Management Tool 606 $aShorelines$xAnalysis$vHandbooks, manuals, etc 606 $aEcology$zOregon$xAnalysis 606 $aCoastal zone management$zOregon 606 $aShore protection$zOregon 606 $aCoastal zone management$2fast 606 $aShore protection$2fast 606 $aShorelines$2fast 607 $aOregon$2fast 608 $aHandbooks and manuals.$2lcgft 615 0$aShorelines$xAnalysis 615 0$aEcology$xAnalysis. 615 0$aCoastal zone management 615 0$aShore protection 615 7$aCoastal zone management. 615 7$aShore protection. 615 7$aShorelines. 700 $aSnyder$b Daniel T.$01398760 702 $aHaluska$b Tana L. 702 $aRespini-Irwin$b Darius 712 02$aGeological Survey (U.S.), 712 02$aUnited States.$bBureau of Land Management. 801 0$bCOP 801 1$bCOP 801 2$bOCLCO 801 2$bOCLCF 801 2$bOCLCA 801 2$bGPO 906 $aBOOK 912 $a9910704648303321 996 $aThe Shoreline Management Tool$93480717 997 $aUNINA LEADER 04715nam 22006255 450 001 9910484631603321 005 20251113175603.0 010 $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 $a20201220d2021 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMalware Analysis Using Artificial Intelligence and Deep Learning /$fedited by Mark Stamp, Mamoun Alazab, Andrii Shalaginov 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XX, 651 p. 253 illus., 209 illus. in color.) 311 08$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 $aComputer crimes 606 $aMachine learning 606 $aComputational intelligence 606 $aData protection 606 $aComputer Crime 606 $aMachine Learning 606 $aComputational Intelligence 606 $aSecurity Services 615 0$aComputer crimes. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aData protection. 615 14$aComputer Crime. 615 24$aMachine Learning. 615 24$aComputational Intelligence. 615 24$aSecurity Services. 676 $a005.84 702 $aAlazab$b Mamoun 702 $aShalaginov$b Andrii 702 $aStamp$b Mark 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484631603321 996 $aMalware analysis using artificial intelligence and deep learning$92814723 997 $aUNINA