Cedric: A Collaborative DDoS Defense System Using Credit

AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems(2023)

引用 0|浏览7
暂无评分
摘要
Distributed denial of service (DDoS) is one of the most common and damaging cyber attacks, and its impact grows rapidly with the massive use of Internet. Collaborative DDoS defense across countries enables faster and more efficient DDoS attack mitigation. Collaboration requires countries that are not target victims to help detect and block the malicious flow, but selfish countries may refuse to do so because lacking individual gain compared with individual cost. In this paper, we model a stochastic game where selfish countries interact repeatedly and form coalitions to defend DDoS attacks. We design a multi-agent system, Cedric, to simulate and solve this complex stochastic game. Each agent adopts Q-learning to find their long-term optimal strategies, and credits are used to encourage efficient collaboration. The Shapley Value based reward assignment of Cedric satisfies several desired properties about fairness and stability. Simulations with trace data of over 7 years' global DDoS attacks support the superiority of Cedric empirically.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络