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Title: Adversarial Classification on Social Networks
The spread of unwanted or malicious content through social me- dia has become a major challenge. Traditional examples of this include social network spam, but an important new concern is the propagation of fake news through social media. A common ap- proach for mitigating this problem is by using standard statistical classi cation to distinguish malicious (e.g., fake news) instances from benign (e.g., actual news stories). However, such an approach ignores the fact that malicious instances propagate through the network, which is consequential both in quantifying consequences (e.g., fake news di using through the network), and capturing de- tection redundancy (bad content can be detected at di erent nodes). An additional concern is evasion attacks, whereby the generators of malicious instances modify the nature of these to escape detection. We model this problem as a Stackelberg game between the defender who is choosing parameters of the detection model, and an attacker, who is choosing both the node at which to initiate malicious spread, and the nature of malicious entities. We develop a novel bi-level programming approach for this problem, as well as a novel solution approach based on implicit function gradients, and experimentally demonstrate the advantage of our approach over alternatives which ignore network structure.  more » « less
Award ID(s):
1649972 1640624 1526860 1905558
NSF-PAR ID:
10050177
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
International Conference on Autonomous Agents and Multiagent Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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