# Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

## TL;DR

This paper proposes a new active, autonomous, and adaptive cyber defense framework called '3A', utilizing strategic learning schemes to improve defense effectiveness under uncertain information conditions.

## Contribution

It introduces three defense schemes with varying information restrictions and applies strategic learning to optimize their policies in uncertain environments.

## Key findings

- All three schemes converge to optimal policies through reinforcement.
- The framework effectively balances security and operational costs.
- It provides a foundation for proactive, strategic cyber defense under incomplete information.

## Abstract

The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the \texttt{`3A'} defense paradigm. This chapter introduces three defense schemes that actively interact with attackers to increase the attack cost and gather threat information, i.e., defensive deception for detection and counter-deception, feedback-driven Moving Target Defense (MTD), and adaptive honeypot engagement. Due to the cyber deception, external noise, and the absent knowledge of the other players' behaviors and goals, these schemes possess three progressive levels of information restrictions, i.e., from the parameter uncertainty, the payoff uncertainty, to the environmental uncertainty. To estimate the unknown and reduce uncertainty, we adopt three different strategic learning schemes that fit the associated information restrictions. All three learning schemes share the same feedback structure of sensation, estimation, and actions so that the most rewarding policies get reinforced and converge to the optimal ones in autonomous and adaptive fashions. This work aims to shed lights on proactive defense strategies, lay a solid foundation for strategic learning under incomplete information, and quantify the tradeoff between the security and costs.