Published Paper Details:

In order to safeguard an organization's Internet security, security information and event management (SIEM) has been implemented to streamline the numerous preventative technologies and signal alerts for security problems. Inspectors of the SOC (Standards of Conduct) are looking into this (SOC). However, the bulk of the warnings are inaccurate, and SCO is unable to handle all of the information. Therefore, it may not be accurate to talk about malicious assaults and compromised hosts. Machine learning might be used to decrease the amount of false positives and enhance the output of SOC analysts. We have created a framework for engineer learning that is user-centered for the Internet Safety Functional Center. The usage of popular data sources, their operation, and how to transform that data into something usable for machine learning are all topics covered in SOC. People who should read this essay fall into one of two categories: Intelligent researchers who have no prior experience with data scientists or computer safety should be the first to develop machine learning methods for machine safety. Currently there are no machine learning experiences available for Internet security experts who have a deep grasp and proficiency in Cyber Security. The last example is a computer built in Seyondike's SOC manufacturing to show all aspects of data collection, label production, feature engineering, and a machine learning algorithm, as well as a sample evaluation of performance.