Visible to the public Toward Limiting Social Botnet Effectiveness while Detection Is Performed: A Probabilistic Approach

TitleToward Limiting Social Botnet Effectiveness while Detection Is Performed: A Probabilistic Approach
Publication TypeConference Paper
Year of Publication2019
AuthorsSpradling, Matthew, Allison, Mark, Tsogbadrakh, Tsenguun, Strong, Jay
Conference Name2019 International Conference on Computational Science and Computational Intelligence (CSCI)
KeywordsBotnet, botnets, compositionality, Computer science, deterministic propagation actions, game theory, invasive software, Metrics, Physics, Probabilistic logic, probabilistic model, probability, pubcrawl, Resiliency, security, social botnet, social botnet effectiveness, social media, social media botnets, social media networks, social media platforms, Social network services, social networking (online), Sociology, Statistics
AbstractThe prevalence of social botnets has increased public distrust of social media networks. Current methods exist for detecting bot activity on Twitter, Reddit, Facebook, and other social media platforms. Most of these detection methods rely upon observing user behavior for a period of time. Unfortunately, the behavior observation period allows time for a botnet to successfully propagate one or many posts before removal. In this paper, we model the post propagation patterns of normal users and social botnets. We prove that a botnet may exploit deterministic propagation actions to elevate a post even with a small botnet population. We propose a probabilistic model which can limit the impact of social media botnets until they can be detected and removed. While our approach maintains expected results for non-coordinated activity, coordinated botnets will be detected before propagation with high probability.
Citation Keyspradling_toward_2019