• VISHNU VARDHAN M S
• SANDHYA M
• SUNNY KUMAR SHARMA
• PRAJWAL P
• Dr. C EMILIN SHYNI

Cybersecurity threats are on the rise due to the increasing use of technology. Phishing causes monetary losses for individuals and organizations. To detect phishing websites, visual similarities-based techniques are used to compare suspicious websites to their legitimate counterparts. Cyberbullying is a form of bullying through electronic messages that has emerged with the exponential increase of social media users. Machine learning approaches can detect language patterns of bullies to generate models that automatically detect cyberbullying actions. Emails can contain sensitive and confidential information, making them valuable to cyber criminals. Phishing attacks are a common strategy used to obtain sensitive information from people by pretending to be from recognized sources. Machine learning algorithms have been utilized to classify emails as phished or ham, achieving high accuracy rates with classifiers such as SVM and Random Forest. Effective machine learning approaches are crucial to combat various cybersecurity threats and protect individuals and organizations.

Cyberbullying, Natural language processing, Phishing detection, Phishing attack, Real time model, Logistic regression.

"CYBERBULLYING AND PHISHING DETECTION ON SOCIAL MEDIA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.e464-e474, May-2023, Available :http://www.jetir.org/papers/JETIR2305464.pdf

"CYBERBULLYING AND PHISHING DETECTION ON SOCIAL MEDIA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppe464-e474, May-2023, Available at : http://www.jetir.org/papers/JETIR2305464.pdf