Cyber Threat Detection Using Machine Learning

Cyber Threat Detection Using Machine Learning

Authors- S. Vetrivel, Assistant Professor Dr. A. Poongodi

Abstract--This research suggests a method employing machine learning techniques for identifying and countering cyber-crimes on social networking platforms. An online application is employed using the Django and Flask frameworks for classifying and visualizing potentially offensive messages, such as instances of cyberstalking and bullying. Supervised machine learning algorithms, i.e., Support Vector Machine (SVM) and Naive Bayes classifiers, are employed for sentiment analysis and threat identification on text data. An interactive dashboard is provided for presenting analysis insights, such as time-series trends of sentiment, topic modeling results, and different affective aspects. The site is made compatible with the likes of Facebook and Twitter and enables proactive identification and moderation of offensive content.

DOI: /10.61463/ijset.vol.13.issue2.475