Wargaming and Capture the Flag (CTF) events have long been used to train and test the skills of cybersecurity professionals. With the advent of AI, a new dimension has been added to these challenges, pitting machines against humans in complex, dynamic scenarios. However, the future of these events lies in the collaboration and teaming of humans and machines, leveraging the strengths of both to create more realistic training environments.
Traditionally, wargaming and CTFs have been designed to simulate real-world cybersecurity scenarios, allowing participants to hone their skills in a controlled environment¹. These events often involve a range of challenges, from network defense and incident response to cryptography and reverse engineering².
AI has begun to play an increasingly significant role in wargaming and CTF events, both as a tool for creating more dynamic and realistic challenges and as a participant in its own right³. Machine learning algorithms can be used to generate adaptive scenarios, respond to participant actions in real-time, and create intelligent, autonomous agents that compete against human players⁴.
Machine participants, powered by AI, bring several advantages to wargaming and CTF events. They can operate at a scale and speed that humans cannot match, processing vast amounts of data and making decisions in real-time⁵. AI agents can learn and adapt their strategies based on the actions of human participants, creating a more dynamic and unpredictable challenge.
On the other hand, human participants bring their own unique strengths to wargaming and CTF events. They possess intuition, creativity, and the ability to think outside the box, enabling them to develop innovative solutions to complex problems⁶. Human players can leverage their experience and understanding of real-world contexts to make informed decisions and anticipate potential risks.
The real power of wargaming and CTFs lies in the collaboration and teaming of human and machine participants. Human-machine teaming can create a more comprehensive and realistic training environment, combining the strengths of both types of participants⁷. Humans can work alongside AI agents, leveraging their unique capabilities to solve complex challenges and develop new strategies. This collaboration can lead to the emergence of novel techniques and approaches that neither humans nor machines could develop independently.
As AI continues to advance, the line between human and machine participants in wargaming and CTFs may become increasingly blurred. The seamless integration and collaboration between human and AI agents, working together to tackle ever-evolving cybersecurity challenges will push the boundaries of what is possible in training and skill dev.
References:
1. Mirkovic, J., & Peterson, P. A. (2014). Class Capture-the-Flag Exercises. In 2014 USENIX Summit on Gaming, Games, and Gamification in Security Education (3GSE 14).
2. Vigna, G., Borgolte, K., Corbetta, J., Doupe, A., Fratantonio, Y., Invernizzi, L., ... & Shoshitaishvili, Y. (2014). Ten years of iCTF: The good, the bad, and the ugly. In 2014 USENIX Summit on Gaming, Games, and Gamification in Security Education (3GSE 14).
3. Holm, H., & Sommestad, T. (2016). SVED: Scanning, vulnerabilities, exploits and detection. In MILCOM 2016-2016 IEEE Military Communications Conference (pp. 976-981). IEEE.
4. Futoransky, A., Notarfrancesco, L., Richarte, G., & Sarraute, C. (2012). Building computer network attacks. arXiv preprint arXiv:1210.5027.
5. Darrah, M., Niemi, B., & Venkatesan, S. (2019). Adversarial Machine Learning for Adaptive Cyberdefense in MACD Framework. In 2019 11th International Conference on Cyber Conflict (CyCon) (pp. 1-16). IEEE.
6. Shoshitaishvili, Y., Invernizzi, L., & Vigna, G. (2013). Do you feel lucky? A large-scale analysis of risk-rewards trade-offs in cyber security. In Proceedings of the 29th Annual Computer Security Applications Conference (pp. 379-390). ACM.
7. Braje, T. M. (2016). Advanced tools for cyber ranges. Lincoln Laboratory Journal, 22(1), 24-32.
