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Published December 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

Prediction of Cybercrime using the Avinashak Algorithm

Creators

  • 1. Research Scholar, Department of Information Technology, Lincoln University College, Malaysia.

Contributors

Contact person:

  • 1. Research Scholar, Department of Information Technology, Lincoln University College, Malaysia.
  • 2. Assistant Professor, Department of Information Technology, Lincoln University College, Malaysia.

Description

Abstract: The detection and prevention of phishing websites continue to be major obstacles in the continually changing field of cybersecurity. Phishing attacks continue to use sophisticated methods to exploit user vulnerabilities, thus it is vital to predict and identify these malicious websites. Traditional techniques for detecting phishing sites frequently rely on rule-based and domain-based approaches, which might not adequately capture the dynamic nature of phishing attacks. The Avinashak Crime Prediction Algorithm appears to be a proprietary or specialized algorithm not widely known in the machine learning community. Its details and working principles are not publicly available, which makes it challenging to provide a detailed explanation without additional information.

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Additional details

Identifiers

DOI
10.54105/ijainn.A1078.124123
EISSN
2582-7626

Dates

Accepted
2023-12-15
Manuscript received on 16 October 2023 | Revised Manuscript received on 13 December 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023

References

  • Aljawarneh, S., Yassein, M. B., & Al-jarrah, O. Y. (2019). A hybrid intelligent system for detecting phishing websites. Journal of King Saud University-Computer and Information Sciences.
  • Aydın, M. A., & Şahin, C. (2019). Machine learning and deep learning models for network intrusion detection systems. IEEE Access, 7, 67779-67788. https://doi.org/10.1109/ACCESS.2019.2893871
  • Yadav, K., Chouhan, D. S., & Mohapatra, D. P. (2019). A survey of malware detection techniques. Journal of Network and Computer Applications, 60, 19-31. https://doi.org/10.1016/j.jnca.2015.11.016
  • Jagatic, T. N., Johnson, N. A., Jakobsson, M., & Menczer, F. (2019). Social phishing. Communications of the ACM, 50(10), 94-100. https://doi.org/10.1145/1290958.1290968
  • Mahjabeen, H., & Hu, J. (2019). A survey of big data architectures and machine learning algorithms in cybersecurity. Journal of King Saud University-Computer and Information Sciences.
  • Alshahrani, M., Hu, J., & Rehman, M. H. (2019). Machine learning in phishing detection: A comprehensive review. IEEE Access, 7, 73662-73676.
  • Khan, I., & Salah, K. (2019). Deep learning for cyber threat intelligence: A survey. IEEE Access, 7, 21171-21184.
  • Fu, Z., Wu, Y., Wang, D., & Li, X. (2019). A novel intrusion detection model based on generative adversarial networks. IEEE Access, 7, 97739-97749.
  • Sood, A. K., Enbody, R. J., & Bansal, A. (2019). Building machine learning models for phishing detection. Journal of Computer Virology and Hacking Techniques, 15(4), 275-287.
  • Raza, S., Kanwal, S., & Nazir, M. (2019). A deep learning approach for network intrusion detection system. Journal of Ambient Intelligence and Humanized Computing, 10(10), 4059-4071.
  • Khyati, & Sohal, Dr. J. S. (2019). A Hybrid Intelligent Decision-Making System for Navigation with Optimized Performance. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 2510–2514). https://doi.org/10.35940/ijitee.j9555.0881019
  • Jebamalar, J. A., & Kumar, Dr. A. S. (2019). PM2.5 Prediction using Machine Learning Hybrid Model for Smart Health. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 6500–6505). https://doi.org/10.35940/ijeat.a1187.109119
  • Mansoori, F. A., & Mishra, Dr. A. (2023). Design of Intelligent Technique for Abnormality Detection in MRI Brain Images. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 11, Issue 5, pp. 77–85). https://doi.org/10.35940/ijrte.e7433.0111523
  • Brahamne, P., Chawla, Assoc. Prof. M. P. S., & Verma, Dr. H. K. (2023). Optimal Sizing of Hybrid Renewable Energy System using Manta Ray Foraging Technique. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 3, pp. 8–16). https://doi.org/10.35940/ijese.c2545.0211323
  • Abualkas, Y. M. A., & Bhaskari, D. L. (2023). Methodologies for Predicting Cybersecurity Incidents. In Indian Journal of Cryptography and Network Security (Vol. 3, Issue 1, pp. 1–8). https://doi.org/10.54105/ijcns.f3677.053123