Indexed In Scopus
  Scopus ID: 21100926589

Cyber Threat Detection: Machine Learning Algorithms for Advanced Security

Ajmeera Kiran and B. Veda Vidhya

Abstract

On social media platforms, it has become increasingly common to persecute people online. It has led to negative outcomes including suicide and despair as a consequence. The policing of content on social media platforms is becoming an increasingly relevant topic. The work that is presented here makes use of Natural Language Processing and machine learning methodologies in order to construct a cyber harassment detection model. This model is built with the help of data obtained from instances of cyber bullying, tweets containing hate speech from Twitter, and personal assaults from Wikipedia. In order to establish which strategy is more effective, we are looking into two feature extraction approaches and six classification algorithms (SVM (Support vector machine), random forest, XGBoost, MLP (Multi-layer perceptron), and logistic regression). This article provides a synopsis of the overall process for identifying instances of cyberbullying as well as the methodology that is most important.

Published on: December 11, 2023
doi: 10.17756/nwj.2023-s4-091
Citation: Kiran A, Vidhya BV. 2023. Cyber Threat Detection: Machine Learning Algorithms for Advanced Security. NanoWorld J 9(S4): S533-S539.

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