Implementation and Monitoring of Network Traffic Security using Machine Learning

Abstract

With the rapid growth of data technology the vast variety of network systems and platforms, as well as the explosive growth of system data, the cloud infrastructure has become increasingly valuable, creating significant safety issues. Cyber hackers’ objectives should be moved by ordinary individuals to internet infrastructures of various backgrounds, corporations, organizations, and nations. Traditional internet production technologies struggled to meet the specific needs of computer safety in terms of reliability and consciousness to the profitability of internet infrastructure, which has resulted in huge quantities of Internet data. Machine learning-based network safety study provided several achievements, demonstrating various applications in big information handling, recent algorithms, detecting the presence, and widening the creation of concepts in the field of network safety. In this article, humans merge computer training technologies to enhance interference detection capability and warning similarity mechanization, as well as examine advanced components such as computer training internet spatial awareness approaches and interactive data stream categorization methods depending on judgment reviews, to enhance machine learning network monitoring innovators’ detection capability, accommodative, but also generalization capabilities.