Using Computational Intelligence for the Dark Web and Illicit Behavior Detection

Using Computational Intelligence for the Dark Web and Illicit Behavior Detection

Romil Rawat (Shri Vaishnav Vidyapeeth Vishwavidyalaya, India), Upinder Kaur (Akal University, Talwandi Sabo, India), Shadab Pasha Khan (Oriental Institute of Science and Technology, Bhopal, India), Ranjana Sikarwar (Amity University, Gwalior, India) and K. Sakthidasan Sankaran (Hindustan Institute of Technology and Science, India)
Release Date: May, 2022|Copyright: © 2022 |Pages: 336
DOI: 10.4018/978-1-6684-6444-1
ISBN13: 9781668464441|ISBN10: 1668464446|EISBN13: 9781668464458|ISBN13 Softcover: 9781668464489
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Description & Coverage
Description:

The Dark Web is a known hub that hosts myriad illegal activities behind the veil of anonymity for its users. For years now, law enforcement has been struggling to track these illicit activities and put them to an end. However, the depth and anonymity of the Dark Web has made these efforts difficult, and as cyber criminals have more advanced technologies available to them, the struggle appears to only have the potential to worsen. Law enforcement and government organizations also have emerging technologies on their side, however. It is essential for these organizations to stay up to date on these emerging technologies, such as computational intelligence, in order to put a stop to the illicit activities and behaviors presented in the Dark Web.

Using Computational Intelligence for the Dark Web and Illicit Behavior Detection presents the emerging technologies and applications of computational intelligence for the law enforcement of the Dark Web. It features analysis into cybercrime data, examples of the application of computational intelligence in the Dark Web, and provides future opportunities for growth in this field. Covering topics such as cyber threat detection, crime prediction, and keyword extraction, this premier reference source is an essential resource for government organizations, law enforcement agencies, non-profit organizations, politicians, computer scientists, researchers, students, and academicians.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Anomaly Detection
  • Anonymous Training
  • Crime Prediction
  • Crime Recognition
  • Cyber Security
  • Cyber Threat Detection
  • Dark Web Threat Evolution
  • Darknet Traffic Analysis
  • Facial Patterns Enhancement
  • Fake Web Content
  • Intimidation Comprehension
  • IoT Botnet Detection
  • Keyword Extraction
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Editor/Author Biographies

Romil Rawat is currently working as Assistant Professor in Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India. He attended several research programs and received research grants from USA, Germany, Italy and UK. The Author has research alignment towards Cyber Security, IoT, Dark Web Crime analysis and investigation techniques, and working towards tracing of illicit anonymous contents of cyber terrorism and criminal activities. He also chaired International Conferences and Hosted several research events including National and International Research Schools, PhD colloquium, Workshops, training programs. He also published several Research Patents.

Upinder Kaur is working as an Assistant Professor in the Department of Computer Science and Engineering, Akal University, Bathinda, Punjab, India. She received her Ph. D. Degree at Department of Computer Science and Applications, Kurukshetra University, Kurukshetra. She is in teaching since October 2006. She holds Master of Technology (M. Tech.) degree in Computer Science and Engineering from MMEC, Mullana, Ambala, India .Her main research interests are in the areas of Distributed Computing, Distributed Data Structures, Cloud Computing, Data Science and Analytics, ML/DL. Currently working on the research issues in applications of deep learning in agriculture and health care. She has attended many National and International Conferences/workshops and she has more than 25 research papers in national / international journals and conferences. She has also a member of IEEE, ACM and supervising three Ph.D. Scholars and several graduate and undergraduate students in multi-cloud domain and data science in agriculture and deep learning in bio-signals.

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