2012/07/24 by Quanyan Zhu, Andrew Clark, Zhu, Quanyan +6 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Computer Science and Game Theory (cs.GT) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Social and Information Networks (cs.SI) #Spam and Phishing Detection #cs.CR #cs.GT #cs.SI
paper · pdf · doi:10.48550/arxiv.1207.5844
arxiv created 2012/07/24 · openalex publication_date 2012/07/24 · arxiv updated 2012/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As social networking sites such as Facebook and Twitter are becoming increasingly popular, a growing number of malicious attacks, such as phishing and malware, are exploiting them. Among these attacks, social botnets have sophisticated infrastructure that leverages compromised users accounts, known as bots, to automate the creation of new social networking accounts for spamming and malware propagation. Traditional defense mechanisms are often passive and reactive to non-zero-day attacks. In this paper, we adopt a proactive approach for enhancing security in social networks by infiltrating botnets with honeybots. We propose an integrated system named SODEXO which can be interfaced with social networking sites for creating deceptive honeybots and leveraging them for gaining information from botnets. We establish a Stackelberg game framework to capture strategic interactions between honeybots and botnets, and use quantitative methods to understand the tradeoffs of honeybots for their deployment and exploitation in social networks. We design a protection and alert system that integrates both microscopic and macroscopic models of honeybots and optimally determines the security strategies for honeybots. We corroborate the proposed mechanism with extensive simulations and comparisons with passive defenses.