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.
Publisher
Lattice Science Publication (LSP)
Reference15 articles.
1. 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.
2. 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
3. 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
4. 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
5. 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.
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