Neural Networks

Data-Driven Model Generation for Deception Defence of Cyber-Physical Environments

Abstract:

Cyber deception is a burgeoning defence technique that provides increased detection and slowed attack impact. Deception could be a valuable solution for defending the slow-to-patch and minimally cryptographic industrial Cyber-Physical Systems. However, it is necessary for cyber- physical decoys to appear connected to the physical process of the defended system to be convincing. In this paper, the authors present a machine-learning approach to learn good-enough models of the defended system to drive realistic decoy response. The results of studying this approach with simulated and real building systems are discussed.

Journal of Information Warfare

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

Keywords

A

AI
APT

C

C2
C2S
CDX
CIA
CIP
CPS

D

DNS
DoD
DoS

I

IA
ICS

S

SOA

X

XRY

Quill Logo

The definitive publication for the best and latest research and analysis on information warfare, information operations, and cyber crime. Available in traditional hard copy or online.

SUBSCRIBE NOW

Get in touch

Principal Office

  • Journal of Information Warfare
  • ArmisteadTEC
  • 525 Landfall Arch,
  • Virginia Beach, VA 23462

Registered Agent and Mailing Address

  • Journal of Information Warfare
  •  ArmisteadTEC
  • Dr Leigh Armistead, President
  • 1624 Wakefield Drive
  • Virginia Beach, VA 23455

 757.510.4574

 JIW@ArmisteadTec.com