2021/10/20 by Thai Le, Thai V. Le, Le, Thai +4
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Misinformation and Its Impacts #Multiagent Systems (cs.MA) #Network Security and Intrusion Detection #Social and Information Networks (cs.SI) #cs.AI #cs.CR #cs.LG #cs.MA #cs.SI
paper · pdf · doi:10.48550/arxiv.2110.10655
Accepted to The ACM Web Conference 2022
openalex publication_date 2021/10/20 · arxiv created 2022/02/26 · arxiv updated 2022/03/01 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
Socialbots are software-driven user accounts on social platforms, acting autonomously (mimicking human behavior), with the aims to influence the opinions of other users or spread targeted misinformation for particular goals. As socialbots undermine the ecosystem of social platforms, they are often considered harmful. As such, there have been several computational efforts to auto-detect the socialbots. However, to our best knowledge, the adversarial nature of these socialbots has not yet been studied. This begs a question "can adversaries, controlling socialbots, exploit AI techniques to their advantage?" To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected. We first formulate the adversarial socialbot learning as a cooperative game between two functional hierarchical RL agents. While one agent curates a sequence of activities that can avoid the detection, the other agent aims to maximize network influence by selectively connecting with right users. Our proposed policy networks train with a vast amount of synthetic graphs and generalize better than baselines on unseen real-life graphs both in terms of maximizing network influence (up to +18%) and sustainable stealthiness (up to +40% undetectability) under a strong bot detector (with 90% detection accuracy). During inference, the complexity of our approach scales linearly, independent of a network's structure and the virality of news. This makes our approach a practical adversarial attack when deployed in a real-life setting.