Cyberspace exercises: defending against malicious cyber actors

Kelly Bennett; James Robertson

doi:10.1117/12.2663514

12 June 2023 Cyberspace exercises: defending against malicious cyber actors

Kelly Bennett, James Robertson

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125380F (2023) https://doi.org/10.1117/12.2663514
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Malicious cyber actors are well-prepared and trained to negatively impact organizations, assets, or individuals. Training exercises help to mitigate these adverse effects by enabling cyber defense analysts to demonstrate their abilities to respond to cyber incidents in real-time. When a security incident occurs, the response team must quickly contain the situation, diagnose the problem, and return to a previous good state so the system down time is minimized, and the issue does not spread to other systems. Training exercises allow both red (attack) and blue (defense) teams to practice in a secure, isolated environment ensuring all team members have the necessary experience if and when incidents occur. This paper describes simulated cybersecurity research scenarios based on open-source data repositories of adversary tactics and techniques including published frameworks by MITRE and NIST. Artificial Intelligence/Machine Learning (AI/ML) techniques and methods supporting cybersecurity research will also be discussed with an emphasis towards cloud environment frameworks. Results from using AL/ML techniques, cloud-based tools and methods, cyber tabletop exercises for cybersecurity research scenarios and use cases will be included.

Citation Download Citation

Kelly Bennett and James Robertson "Cyberspace exercises: defending against malicious cyber actors", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125380F (12 June 2023); https://doi.org/10.1117/12.2663514

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available