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Original Article
Ontology-Based Domain Framework for Enhanced Anti-Phishing Measures
Sana Khan1
Mani Priya Mishra2
Rukaiya Khatoon3
123 B. Tech Student, Department of Computer Science, Institute of technology and Management (ITM), Gida, Gorakhpur, Uttar Pradesh, India.
Published Online: November-December 2024
Pages: 01-03
Cite this article
No DOIReferences
. Abu-Salih, B.; Qudah, D.A.; Al-Hassan, M.; Ghafari, S.M.; Issa, T.; Aljarah, I.; Alqahtani, S. An intelligent system for multi-topic social
spam detection in microblogging. J. Inf. Sci. 2022.
2. Zantal-Wiener, A. 47% of Social Media Users Report Seeing More Spam in Their Feeds, even as Networks Fight to Stop It. 2019.
3. Barati, R. Security Threats and Dealing with Social Networks. SN Comput. Sci. 2022, 4, 9.
4. Rodrigues, A.P.; Fernandes, R.; Shetty, A.; Lakshmanna, K.; Shafi, R.M. Real-time twitter spam detection and sentiment analysis using
machine learning and deep learning techniques. Comput. Intell. Neurosci. 2022, 2022, 5211949.
spam detection in microblogging. J. Inf. Sci. 2022.
2. Zantal-Wiener, A. 47% of Social Media Users Report Seeing More Spam in Their Feeds, even as Networks Fight to Stop It. 2019.
3. Barati, R. Security Threats and Dealing with Social Networks. SN Comput. Sci. 2022, 4, 9.
4. Rodrigues, A.P.; Fernandes, R.; Shetty, A.; Lakshmanna, K.; Shafi, R.M. Real-time twitter spam detection and sentiment analysis using
machine learning and deep learning techniques. Comput. Intell. Neurosci. 2022, 2022, 5211949.
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