Cyber preparation of the environment (CPE) has emerged as a proactive strategy for defenders to gain a strategic advantage against potential threats in the ever-evolving cybersecurity landscape. By actively shaping the perception of the attack surface and employing techniques such as pre-poisoning adversarial AI models, organizations can significantly enhance their defensive capabilities and mitigate the impact of potential breaches.
One of the key aspects of CPE is the manipulation of the visible attack surface to deceive and mislead adversaries. By leveraging techniques such as honeypots, decoys, and obfuscation, defenders can create a false perception of the network topology and vulnerabilities, luring adversaries into focusing their efforts on less critical or well-defended areas¹. This carefully crafted view of the attack surface not only wastes the adversaries' time and resources but also allows defenders to gather valuable intelligence on their tactics, techniques, and procedures (TTPs)².
Another crucial component of CPE is the pre-poisoning of adversarial AI models. As adversaries increasingly rely on machine learning and AI to automate and optimize their attacks, defenders can proactively manipulate the data used to train these models³. By introducing carefully designed data points into the training process, defenders can induce biases and errors that degrade the effectiveness of AI-driven attacks. This pre-poisoning technique can significantly reduce the success rate of adversarial AI and limit the impact of potential breaches⁴.
However, implementing CPE strategies is not without its challenges. Defenders must possess a deep understanding of adversarial AI and the ability to craft convincing deceptions to effectively shape the attack surface perception and pre-poison AI models.
As the cybersecurity landscape continues to evolve, the development of advanced deception techniques and more robust pre-poisoning methods will be critical for maintaining an effective defense against emerging threats.
Cyber preparation of the environment, through the shaping of attack surface perception and pre-poisoning of adversarial AI, offers a powerful proactive approach to cybersecurity. By actively manipulating the adversary's perception and undermining the effectiveness of their AI-driven attacks, defenders can gain a strategic advantage and significantly enhance their ability to protect critical assets. As the battle between defenders and adversaries continues to unfold in the digital realm, the adoption of CPE strategies will be crucial for organizations seeking to maintain a robust cybersecurity posture in the face of ever-evolving threats.
References:
¹ Heckman, K. E., Stech, F. J., Thomas, R. K., Schmoker, B., & Tsow, A. W. (2015). Cyber Denial, Deception and Counter Deception: A Framework for Supporting Active Cyber Defense. Springer International Publishing.
² Fraunholz, D., Krohmer, D., Anton, S. D., & Schotten, H. D. (2017). On the Detection of Honeypot Deployment via Virtual Sensor Nodes. Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP), 498-505.
³ Kaloudi, N., & Li, J. (2020). The AI-Based Cyber Threat Landscape: A Survey. ACM Computing Surveys, 53(1), 1-34.
⁴ Duddu, V., & Samanta, D. (2020). A Survey of Adversarial Machine Learning and Cyber Security. Journal of Defense Modeling and Simulation, 17(1), 99-120.
⁵ Han, X., Pasquier, T., Ranjan, T., Goldstein, M., & Seltzer, M. (2020). SIGL: Securing Software Installations Through Deep Graph Learning. Proceedings of the 29th USENIX Security Symposium, 1723-1740.
⁶ Jiang, Y., Hao, S., & Zhang, Q. (2021). Poisoning Attacks on AI-Based Network Intrusion Detection Systems. IEEE Access, 9, 62384-62393.