Development of Big Data Extraction and Learning Platform for Packet Analysis in Industrial Control Syste

EasyChair Preprint 1276

Abstract

The Industrial Control System (ICS) uses industrial structure and shape control systems and related systems such as industrial process operations and automation equipment, systems, networks and control devices. The configuration of a PLC (Programmable Logic Controller) must manage the system and manage the environment. Various industrial control system protocols based on fieldbus and Ethernet work. However, attacks targeting industrial control systems such as Stuxnet, Black Energy, TRISIS-TRITION, and IRONGATE may perform abnormal operations on each device such as EWS (Engineering workstation), HMI (Human Machine Interface) and SIS (Safety instrumented system) Data collection and destruction, substation shutdown, and so on. However, due to the nature of the closed industrial control system, it is relatively difficult to analyze the vulnerability of each system and secure data sets for proactive response. In this paper, we propose an intelligent industrial control system framework that provides integrated management and automatic control of data by using raspberry pie and various sensors to prepare for attack against industrial control system, We propose an artificial intelligence industrial control system that can cope with dictionary attacks through normalization learning

Keyphrases: Artificial Intelligence, Big Data Analysis, Industrial Control System, Security testbed, vulnerability evaluation

@booklet{EasyChair:1276,
  author    = {Kangbin Yim and Juyoung Seo and Chanmin Kim and Dain Kim and Soyoung Jung},
  title     = {Development of Big Data Extraction and Learning Platform for Packet Analysis in Industrial Control Syste},
  howpublished = {EasyChair Preprint 1276},
  year      = {EasyChair, 2019}}