Defending via strategic ML selection
This addresses security vulnerabilities in ML systems for applications where adversaries can tamper with data, though it appears incremental as it builds on existing game-theoretic concepts.
The paper tackles the problem of adversarial data manipulation in machine learning by proposing a strategic defense mechanism that uses an array of learners activated based on game-theoretic strategies, such as dominant or Nash-stable strategies, to counteract attacks.
The results of a learning process depend on the input data. There are cases in which an adversary can strategically tamper with the input data to affect the outcome of the learning process. While some datasets are difficult to attack, many others are susceptible to manipulation. A resourceful attacker can tamper with large portions of the dataset and affect them. An attacker can additionally strategically focus on a preferred subset of the attributes in the dataset to maximize the effectiveness of the attack and minimize the resources allocated to data manipulation. In light of this vulnerability, we introduce a solution according to which the defender implements an array of learners, and their activation is performed strategically. The defender computes the (game theoretic) strategy space and accordingly applies a dominant strategy where possible, and a Nash-stable strategy otherwise. In this paper we provide the details of this approach. We analyze Nash equilibrium in such a strategic learning environment, and demonstrate our solution by specific examples.