A Method Based on Machine Learning to Classify Text for the Field of Cybersecurity

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Rapid advancements in networks and computer systems have opened a new door for immoral acts like cybercrime, which threaten public safety, and security, as well as the global economy. The purpose of this proposal is to analyse IP fraud and cyberbullying as two distinct types of cybercrime. The primary goals of this study are to use instances of cybercrime to provide a short examination of cybercrime activities, and the family member principles, and propose a pairing schema. Using the Naive Bayes (NB) & Support Vector Machine (SVM) artificial intelligence techniques, cybercrime instances are categorised according to their ideal qualities. The Twitter data in the Kaggle database has been clustered using K-means. User ID, sign-up date, referral, browser, gender, and age as well as IP address are just a few of the most useful information used to educate the computer. Total 151,113 datasets were used for experimental analysis of the suggested algorithm's performance. The accuracy of the suggested approach, 97%, is higher than that of the current method (NB). The challenge of regression may be easily surmounted with the use of the random forest method for the categorization of the resultant cybercrimes. The planned study uses age categories as the foundation for identifying the different offenses.