Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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A Data Mining Approach to Monitor Terrorism Dissemination Online

Authors
M. Asha Priyadarshini1, T. V. L. Bhavani2, *, P. Geya Geeta Sree2, S. K. Darga Mastan Vali2, P. Ashok Chakravarthi2
1Associate Professor, Department of CSE, Vignan’s Lara Institute of Technology & Science, Vadlamudi, Guntur, Andhra Pradesh, India
2UG Scholar, Department of CSE, Vignan’s Lara Institute of Technology & Science, Vadlamudi, Guntur, Andhra Pradesh, India
*Corresponding author. Email: thummalapallibhavani13@gmail.com
Corresponding Author
T. V. L. Bhavani
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_76How to use a DOI?
Keywords
Web data mining; terrorism detection; machine learning techniques; XGBoost; Gradient Boosting; Adaboost; SVM; Random Forest; feature extraction; website analysis; cybersecurity; global security
Abstract

Web data mining is essential for identifying the online propagation of terrorism. Terrorist groups are using phishing websites more frequently to spread their beliefs, find new members, and plan events. We can evaluate web data to differentiate between websites linked to terrorist activity and those that are legal by using machine learning algorithms like XGBoost, Gradient Boosting, Adaboost, SVM, and Random Forest. These algorithms are capable of efficiently identifying suspicious patterns suggestive of sites linked to terrorism by extracting data such as URL structure, domain age, and content. We can determine the precision and effectiveness of these techniques by conducting a thorough assessment, which will allow us to take preventative action like blocking locations known to be used by terrorists. Web data mining, terrorism detection, machine learning techniques, XGBoost, Gradient Boosting, Adaboost, SVM, Random Forest, feature extraction, website analysis, cybersecurity, global security.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_76How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. Asha Priyadarshini
AU  - T. V. L. Bhavani
AU  - P. Geya Geeta Sree
AU  - S. K. Darga Mastan Vali
AU  - P. Ashok Chakravarthi
PY  - 2024
DA  - 2024/07/30
TI  - A Data Mining Approach to Monitor Terrorism Dissemination Online
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 794
EP  - 803
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_76
DO  - 10.2991/978-94-6463-471-6_76
ID  - Priyadarshini2024
ER  -