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    <title>Using computational intelligence for the dark web and illicit behavior detection</title>
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  <name type="personal">
    <namePart>Rawat, Romil</namePart>
    <namePart type="date">1986-</namePart>
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    <dateIssued encoding="marc">2022</dateIssued>
    <issuance>monographic</issuance>
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  <physicalDescription>
    <extent>1 online resource (xviii, 335 pages) : illustrations (chiefly color).</extent>
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  <abstract>"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"--</abstract>
  <tableOfContents>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.</tableOfContents>
  <note type="statement of responsibility">[edited by] Romil Rawat... [and 4 others].</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Anomaly detection (Computer security)</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Intrusion detection systems (Computer security)</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Computer networks</topic>
    <topic>Monitoring</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Computer crimes</topic>
    <topic>Prevention</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Dark Web</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
  </subject>
  <classification authority="lcc">TK5105.59 .U77 2022</classification>
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      <title>Using computational intelligence for the dark web and illicit behavior detection</title>
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      <publisher>Hershey, PA : Information Science Reference, an imprint of IGI Global, [2023]</publisher>
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    <identifier type="local">(DLC)  2022020091</identifier>
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      <title>Advances in digital crime, forensics, and cyber terrorism (ADCFCT) book series</title>
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  <identifier type="isbn">9781668464458</identifier>
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