AN ANALYSIS OF RACIALISM IN TWITTER DATASET THROUGH WEBPAGE BASED ON TRUTH CLASSIFICATION AND VIRTUALIZATION

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Monitoring social media and Internet traffic, as well as cyberattack detection and protection, is crucial. Automated, machine-learning technologies are progressively replacing conventional methods that use manually established criteria. Large data sets that allow machine learning algorithms with outstanding performance have accelerated the change. The article discusses the most recent analysis of internet and social network traffic using a variety of common concepts including similarity, correlation, and collective indication as well as security goals for categorising network hosts, programmes, users, and tweets. The study demonstrates a cutting-edge method for analysing social media and Internet traffic while studying data-driven cyber security. The three elements of the cyber security strategy are cyber security modelling, cyber safety engineering, and cyber security. Discussions on the difficulties and potential possibilities for the field will also take place. Data-driven cyber security solutions that are used to networks with tens of thousands of edges continue to provide theoretical assurance on the quality of the solution. An algorithm for learning to develop a striking oneself to avoid walking is an evaluation of research. Low memory requirements enable scheduling of millions of threads to disguise latency and manage massive networks.

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Keywords

Cyber Security, Data Mining, Classification, Virtualization.