Detection of malicious behavior is a fundamental problem in security. One of the major challenges in using detection systems in practice is in dealing with an overwhelming number of alerts that are triggered by normal behavior (the socalled false positives), obscuring alerts resulting from actual malicious activity. While numerous methods for reducing the scope of this issue have been proposed, ultimately one must still decide how to prioritize which alerts to investigate, and most existing prioritization methods are heuristic, for example, based on suspiciousness or priority scores. We introduce a novel approach for computing a policy for prioritizing alerts using adversarial reinforcement learning. Our approach assumes that the attackers know the full state of the detection system and dynamically choose an optimal attack as a function of this state, as well as of the alert prioritization policy. The first step of our approach is to capture the interaction between the defender and attacker in a game theoretic model. To tackle the computational complexity of solving this game to obtain a dynamic stochastic alert prioritization policy, we propose an adversarial reinforcement learning framework. In this framework, we use neural reinforcement learning to compute best response policies for both the defender andmore »
Robust Collective Classification against Structural Attacks
Collective learning methods exploit relations
among data points to enhance classification
performance. However, such relations, represented as edges in the underlying graphical
model, expose an extra attack surface to the
adversaries. We study adversarial robustness
of an important class of such graphical models, Associative Markov Networks (AMN), to
structural attacks, where an attacker can modify the graph structure at test time. We formulate the task of learning a robust AMN
classifier as a bilevel program, where the inner problem is a challenging nonlinear integer program that computes optimal structural
changes to the AMN. To address this technical challenge, we first relax the attacker problem, and then use duality to obtain a convex
quadratic upper bound for the robust AMN
problem. We then prove a bound on the quality of the resulting approximately optimal solutions, and experimentally demonstrate the
efficacy of our approach. Finally, we apply
our approach in a transductive learning setting,
and show that robust AMN is much more robust than stateoftheart deep learning methods, while sacrificing little in accuracy on nonadversarial data.
 Publication Date:
 NSFPAR ID:
 10173998
 Journal Name:
 Conference on Uncertainty in Artificial Intelligence (UAI)
 Sponsoring Org:
 National Science Foundation
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