Machine Learning Models in Detecting Cyber Crimes and Cyber Terrorism in India

Abstract

Cyber-physical systems (CPSs), which are more susceptible to a range of cyber-attacks, play an increasingly crucial role in power system security today. Digital communication has become a global phenomenon in the last decade. Sadly, cyber terrorism is on the rise, and abusers are able to hide behind the anonymity of the internet. A hybrid model for detecting instances of cyber terrorism in Twitter datasets was proposed in this study after a survey of prominent classification algorithms. Logistic regression, linear support vector classifier, and naive bayes are the methods utilised for evaluation. Four metrics were used to evaluate the performance of the classifiers in experiments: precision, F1, accuracy, and recall. The findings show how well each of the algorithms worked, along with the metrics that went along with them. Linear support vector classifier (SVC) was the least effective, while hybrid model (EM) was the most successful.