04940nam a2200589 i 4500 on1334652771 EBZ 20251029164101.0 m d cr cnu---unuuu 220520s2022 paua ob 001 0 eng 2022020092 EBZ eng EBZ 9781668464458 electronic book 1668464454 electronic book 9781668464441 hardcover 1668464446 hardcover 9781668464489 paperback 1668464489 paperback pcc TK5105.59 .U77 2022 MAIN Using computational intelligence for the dark web and illicit behavior detection / [edited by] Romil Rawat... [and 4 others]. Hershey, PA : IGI Global, Information Science Reference (an imprint of IGI Global), [2022] 1 online resource (xviii, 335 pages) : illustrations (chiefly color). text txt rdacontent computer c rdamedia online resource cr rdacarrier textual sapdv EBSCOhost data file rda Advances in digital crime, forensics, and cyber terrorism (ADCFCT) book series Includes bibliographical references and index. "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"-- Provided by publisher. 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. EBSCOhost "EBSCO evaluates our products based on the Web Content Accessibility Guidelines (WCAG) and the related Section 508 and EN 301 549 regulations in the US and EU. Most EBSCO products are substantially conformant with WCAG 2.2 level AA." Source: https://connect.ebsco.com/s/article/EBSCO-VPATs?language=en_US. Last accessed April 22, 2025. Description based on online resource; title from digital title page (viewed on August 09, 2022). Anomaly detection (Computer security) 64382 Intrusion detection systems (Computer security) 64383 Computer networks Monitoring. 64384 Computer crimes Prevention. 64385 Dark Web. 59051 Artificial intelligence. 58441 Rawat, Romil, 1986- editor. 64386 Print version: Using computational intelligence for the dark web and illicit behavior detection Hershey, PA : Information Science Reference, an imprint of IGI Global, [2023] 9781668464441 (DLC) 2022020091 Advances in digital crime, forensics, and cyber terrorism (ADCFCT) book series. 64387 EBSCOhost https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3336441 IGI Global IGIG 00301614 EBSCOhost EBSC 3336441 92 N$T 55979 55979