Using computational intelligence for the dark web and illicit behavior detection:
"The goal of this monograph is to convey a picture of the Dark Web environment, suggest a methodical, computational approach to comprehending the difficulties, and define tactics and solutions, providing researchers, security professionals, counter-terrorism specialists, and strategy developers...
Gespeichert in:
| Weitere Verfasser: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
2023.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "The goal of this monograph is to convey a picture of the Dark Web environment, suggest a methodical, computational approach to comprehending the difficulties, and define tactics and solutions, providing researchers, security professionals, counter-terrorism specialists, and strategy developers with useful information." |
| Beschreibung: | 27 PDFs (336 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781668464458 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
MARC
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| 245 | 0 | 0 | |a Using computational intelligence for the dark web and illicit behavior detection |c Romil Rawat, Upinder Kaur, Shadab Khan, Ranjana Sikarwar, and K. Sakthidasan Sankaran, editors. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : |b IGI Global, |c 2023. | |
| 300 | |a 27 PDFs (336 pages) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Section 1. Traffic analysis and network management. Chapter 1. Darknet traffic analysis and network management for malicious intent detection by neural network frameworks ; Chapter 2. Information system for cyber threat detection using K-NN classification model ; Chapter 3. Structure analysis of tor hidden services using DOM-inspired graphs -- Section 2. Models for crime prediction. Chapter 4. Time-series models for crime prediction in India: an empirical comparison ; Chapter 5. Variational autoencoder for IoT botnet detection ; Chapter 6. Cyber security for IoT-enabled industry 4.0: systematic review for dark web environments -- Section 3. Anomaly detection using artificial intelligence and machine learning algorithms. Chapter 7. Artifical intelligence and machine learning algorithms in dark web crime recognition ; Chapter 8. Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques ; Chapter 9. Anomaly detection in biometric authentication dataset using recurrent neural networks -- Section 4. Content labeling for hidden services using keyword extraction. Chapter 10. Content labelling of hidden services with keyword extraction using the graph decomposition method ; Chapter 11. Tapering malicious language for identifying fake web content ; Chapter 12. Cyber security event sentence detection from news articles based on trigger and argument -- Section 5. Assessment of dark web threat evolution. Chapter 13. Systematic approach for detection and assessment of dark web threat evolution ; Chapter 14. Enthusiastic cyber surveillance for intimidation comprehension on the dark web and the deep web ; Chapter 15. Anonymous trading on the dark online marketplace: an exploratory study. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "The goal of this monograph is to convey a picture of the Dark Web environment, suggest a methodical, computational approach to comprehending the difficulties, and define tactics and solutions, providing researchers, security professionals, counter-terrorism specialists, and strategy developers with useful information." | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 07/17/2022). | ||
| 650 | 0 | |a Anomaly detection (Computer security) | |
| 650 | 0 | |a Computer networks |x Monitoring. | |
| 650 | 0 | |a Intrusion detection systems (Computer security) | |
| 700 | 1 | |a Kaur, Upinder, |e editor. | |
| 700 | 1 | |a Khan, Shadab, |e editor. | |
| 700 | 1 | |a Rawat, Romil, |e editor. | |
| 700 | 1 | |a Sankaran, K Sakthidasan, |e editor. | |
| 700 | 1 | |a Sikarwar, Ranjana, |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
| 776 | 0 | 8 | |i Print version: |z 1668464446 |z 9781668464441 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-98-IGB |q FWS_PDA_IGB |u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6444-1 |3 Volltext |
| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-98-IGB-00301614 |
|---|---|
| _version_ | 1816797086729895936 |
| adam_text | |
| any_adam_object | |
| author2 | Kaur, Upinder Khan, Shadab Rawat, Romil Sankaran, K Sakthidasan Sikarwar, Ranjana |
| author2_role | edt edt edt edt edt |
| author2_variant | u k uk s k sk r r rr k s s ks kss r s rs |
| author_facet | Kaur, Upinder Khan, Shadab Rawat, Romil Sankaran, K Sakthidasan Sikarwar, Ranjana |
| building | Verbundindex |
| bvnumber | localFWS |
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| collection | ZDB-98-IGB |
| contents | Section 1. Traffic analysis and network management. Chapter 1. Darknet traffic analysis and network management for malicious intent detection by neural network frameworks ; Chapter 2. Information system for cyber threat detection using K-NN classification model ; Chapter 3. Structure analysis of tor hidden services using DOM-inspired graphs -- Section 2. Models for crime prediction. Chapter 4. Time-series models for crime prediction in India: an empirical comparison ; Chapter 5. Variational autoencoder for IoT botnet detection ; Chapter 6. Cyber security for IoT-enabled industry 4.0: systematic review for dark web environments -- Section 3. Anomaly detection using artificial intelligence and machine learning algorithms. Chapter 7. Artifical intelligence and machine learning algorithms in dark web crime recognition ; Chapter 8. Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques ; Chapter 9. Anomaly detection in biometric authentication dataset using recurrent neural networks -- Section 4. Content labeling for hidden services using keyword extraction. Chapter 10. Content labelling of hidden services with keyword extraction using the graph decomposition method ; Chapter 11. Tapering malicious language for identifying fake web content ; Chapter 12. Cyber security event sentence detection from news articles based on trigger and argument -- Section 5. Assessment of dark web threat evolution. Chapter 13. Systematic approach for detection and assessment of dark web threat evolution ; Chapter 14. Enthusiastic cyber surveillance for intimidation comprehension on the dark web and the deep web ; Chapter 15. Anonymous trading on the dark online marketplace: an exploratory study. |
| ctrlnum | (CaBNVSL)slc00002928 (OCoLC)1334652771 |
| dewey-full | 005.8 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.8 |
| dewey-search | 005.8 |
| dewey-sort | 15.8 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-98-IGB-00301614 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:51:59Z |
| institution | BVB |
| isbn | 9781668464458 |
| language | English |
| oclc_num | 1334652771 |
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| physical | 27 PDFs (336 pages) Also available in print. |
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| spelling | Using computational intelligence for the dark web and illicit behavior detection Romil Rawat, Upinder Kaur, Shadab Khan, Ranjana Sikarwar, and K. Sakthidasan Sankaran, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, 2023. 27 PDFs (336 pages) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Section 1. Traffic analysis and network management. Chapter 1. Darknet traffic analysis and network management for malicious intent detection by neural network frameworks ; Chapter 2. Information system for cyber threat detection using K-NN classification model ; Chapter 3. Structure analysis of tor hidden services using DOM-inspired graphs -- Section 2. Models for crime prediction. Chapter 4. Time-series models for crime prediction in India: an empirical comparison ; Chapter 5. Variational autoencoder for IoT botnet detection ; Chapter 6. Cyber security for IoT-enabled industry 4.0: systematic review for dark web environments -- Section 3. Anomaly detection using artificial intelligence and machine learning algorithms. Chapter 7. Artifical intelligence and machine learning algorithms in dark web crime recognition ; Chapter 8. Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques ; Chapter 9. Anomaly detection in biometric authentication dataset using recurrent neural networks -- Section 4. Content labeling for hidden services using keyword extraction. Chapter 10. Content labelling of hidden services with keyword extraction using the graph decomposition method ; Chapter 11. Tapering malicious language for identifying fake web content ; Chapter 12. Cyber security event sentence detection from news articles based on trigger and argument -- Section 5. Assessment of dark web threat evolution. Chapter 13. Systematic approach for detection and assessment of dark web threat evolution ; Chapter 14. Enthusiastic cyber surveillance for intimidation comprehension on the dark web and the deep web ; Chapter 15. Anonymous trading on the dark online marketplace: an exploratory study. Restricted to subscribers or individual electronic text purchasers. "The goal of this monograph is to convey a picture of the Dark Web environment, suggest a methodical, computational approach to comprehending the difficulties, and define tactics and solutions, providing researchers, security professionals, counter-terrorism specialists, and strategy developers with useful information." Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 07/17/2022). Anomaly detection (Computer security) Computer networks Monitoring. Intrusion detection systems (Computer security) Kaur, Upinder, editor. Khan, Shadab, editor. Rawat, Romil, editor. Sankaran, K Sakthidasan, editor. Sikarwar, Ranjana, editor. IGI Global, publisher. Print version: 1668464446 9781668464441 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6444-1 Volltext |
| spellingShingle | Using computational intelligence for the dark web and illicit behavior detection Section 1. Traffic analysis and network management. Chapter 1. Darknet traffic analysis and network management for malicious intent detection by neural network frameworks ; Chapter 2. Information system for cyber threat detection using K-NN classification model ; Chapter 3. Structure analysis of tor hidden services using DOM-inspired graphs -- Section 2. Models for crime prediction. Chapter 4. Time-series models for crime prediction in India: an empirical comparison ; Chapter 5. Variational autoencoder for IoT botnet detection ; Chapter 6. Cyber security for IoT-enabled industry 4.0: systematic review for dark web environments -- Section 3. Anomaly detection using artificial intelligence and machine learning algorithms. Chapter 7. Artifical intelligence and machine learning algorithms in dark web crime recognition ; Chapter 8. Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques ; Chapter 9. Anomaly detection in biometric authentication dataset using recurrent neural networks -- Section 4. Content labeling for hidden services using keyword extraction. Chapter 10. Content labelling of hidden services with keyword extraction using the graph decomposition method ; Chapter 11. Tapering malicious language for identifying fake web content ; Chapter 12. Cyber security event sentence detection from news articles based on trigger and argument -- Section 5. Assessment of dark web threat evolution. Chapter 13. Systematic approach for detection and assessment of dark web threat evolution ; Chapter 14. Enthusiastic cyber surveillance for intimidation comprehension on the dark web and the deep web ; Chapter 15. Anonymous trading on the dark online marketplace: an exploratory study. Anomaly detection (Computer security) Computer networks Monitoring. Intrusion detection systems (Computer security) |
| title | Using computational intelligence for the dark web and illicit behavior detection |
| title_auth | Using computational intelligence for the dark web and illicit behavior detection |
| title_exact_search | Using computational intelligence for the dark web and illicit behavior detection |
| title_full | Using computational intelligence for the dark web and illicit behavior detection Romil Rawat, Upinder Kaur, Shadab Khan, Ranjana Sikarwar, and K. Sakthidasan Sankaran, editors. |
| title_fullStr | Using computational intelligence for the dark web and illicit behavior detection Romil Rawat, Upinder Kaur, Shadab Khan, Ranjana Sikarwar, and K. Sakthidasan Sankaran, editors. |
| title_full_unstemmed | Using computational intelligence for the dark web and illicit behavior detection Romil Rawat, Upinder Kaur, Shadab Khan, Ranjana Sikarwar, and K. Sakthidasan Sankaran, editors. |
| title_short | Using computational intelligence for the dark web and illicit behavior detection |
| title_sort | using computational intelligence for the dark web and illicit behavior detection |
| topic | Anomaly detection (Computer security) Computer networks Monitoring. Intrusion detection systems (Computer security) |
| topic_facet | Anomaly detection (Computer security) Computer networks Monitoring. Intrusion detection systems (Computer security) |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-6444-1 |
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