Cloud Data Platform

Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems

Abstract: 

Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data platform service. Adversarial examples are malicious inputs to ML-models that provide erroneous model outputs while appearing to be unmodified. This kind of attack can fool the classifier and can prevent ML-models from generalizing well and from learning high-level representation; instead, the ML-model learns superficial dataset regularity. This study focuses on investigating, detecting, and preventing adversarial attacks towards a cloud data platform in the cyber-physical context.

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

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

  • Journal of Information Warfare
    114 Ballard Street
    Yorktown, VA
    23690
  • 757.871.3949
  • jiw@gbpts.com