Abstract
In this contemporary cyber world, grid systems play a crucial role. In those grids, security aspectis always prime. Supervisory Control and Data Acquisition (SCADA) is the centralized system that controlthe entire grid. When a system is considered to be a whole and sole control of huge grid then obviously anuncompromised security would be the vital. By having that as a major concern a lot of research is beingdone on IDS security. In spite of that it has several cons including increased fake positive and fake negativerates, which will invariably leads to a larger chaos. This research work is actually a prework to implementmachine learning algorithms in the SCADA data set to segregate testing and training data and to furtheroptimize. So a real time SCADA data set collected from the test bed is tested with two major tools netstatand snort. These tools helps in analysing the entire network traffic and it also helps in finding any anomaly /outliers present inside the network. This research work helps in scrutinizing the network data by finding outthe intruders not just stopping by that but also add the attack in the library database for future use.
Shitharth Selvarajan
Lecturer in Cyber Security
My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.