Published August 7, 2019 | Version v1
Report

A Passive Network Cyber Threat Intelligence Framework for Legacy Critical Control Systems using Machine Learning

  • 1. Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Laboratory (SRNL)

Description

The importance of Internet and communication networks in our daily life and in any organization's daily operations is well known and cannot be overstressed. A nation's economy is fully reliant on its critical infrastructure. Energy sector is one of the 16 Critical Infrastructure Sectors identified by the Department of Homeland Security. Securing these critical infrastructure sectors is challenging but is also of utmost priority in this day of constant and persistent cyber threats. Threat is any circumstance or event that has the potential to adversely impact an agency's assets and operations. Cyber Threat Intelligence (CTI) is the process of collection, analysis, and identification of potential cyber threats to the organization. This goal of current research performed at the Savannah River National Laboratory (SRNL), Aiken, SC, is to develop a Cyber Threat Intelligence framework for gathering Threat Intelligence passively from the network traffic from and to a real or simulated Critical Control Systems.

Availability note (English)

Available from https://www.osti.gov/servlets/purl/1547280; https://www.osti.gov/biblio/1547280; DOE Accepted Manuscript full text, or the publishers Best Available Version will be available free of charge after the embargo period

Additional details

Publishing Information

Imprint Pagination
10 p.
Report number
SRNL-STI--2019-00455

INIS

Country of Publication
United States
Country of Input or Organization
United States
INIS RN
54044394
Subject category
S97: MATHEMATICAL METHODS AND COMPUTING;
Resource subtype / Literary indicator
Non-conventional Literature
Descriptors DEI
COMMUNICATIONS; COMPUTERIZED SIMULATION; CONTROL SYSTEMS; MACHINE LEARNING; SECURITY
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING; MATHEMATICAL LOGIC; SIMULATION

Optional Information

Contract/Grant/Project number
AC09-08SR22470
Funding organization
USDOE Office of Environmental Management - EM (United States)
Secondary number(s)
OSTIID--1547280