A Feasibility Study on the Application of the ScriptGenE Framework as an Anomaly Detection System in Industrial Control Systems

Abstract

Recent events such as Stuxnet and the Shamoon Aramco have brought to light how vulnerable industrial control systems (ICSs) are to cyber attacks. Modern society relies heavily on critical infrastructure, including the electric power grid, water treatment facilities, and nuclear energy plants. Malicious attempts to disrupt, destroy and disable such systems can have devastating e ects on a populations way of life, possibly leading to loss of life. The need to implement security controls in the ICS environment is more vital than ever. ICSs were not originally designed with network security in mind. Today, intrusion detection systems are employed to detect attacks that penetrate the ICS network. This research proposes the use of a novel algorithm known as the ScriptGenE framework as an anomaly-based intrusion detection system. The anomaly detection system (ADS) is implemented between an engineering workstation and programmable logic controller to monitor tra c and alert the operator to anomalous behavior. The ADS achieves true positive rates of 0.9011 and 1.00 with false positive rates of 0 and 0.054. This research demonstrates the viability of using the ScriptGenE framework as an anomaly detection system in a simulated ICS environment.

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Document Details

Document Type
Technical Report
Publication Date
Sep 17, 2015
Accession Number
ADA622349

Entities

People

  • Charito M. Corvin

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Application Protocols
  • Computer Network Security
  • Computers
  • Cyberattacks
  • Cybersecurity
  • Denial Of Service Attack
  • Detection
  • Detectors
  • Graphical User Interface
  • Human-Machine Interfaces
  • Intrusion Detection
  • Intrusion Detectors
  • Machine Learning
  • Network Protocols
  • Operating Systems
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Cybersecurity.

Technology Areas

  • Cyber