He Xiaofei, Yang Xinyu, Yu Wei, Lin Jie, Yang Qingyu
Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA.
Sensors (Basel). 2018 Feb 24;18(2):674. doi: 10.3390/s18020674.
Diverse and varied cyber-attacks challenge the operation of the smart-world system that is supported by Internet-of-Things (IoT) (smart cities, smart grid, smart transportation, etc.) and must be carefully and thoughtfully addressed before widespread adoption of the smart-world system can be fully realized. Although a number of research efforts have been devoted to defending against these threats, a majority of existing schemes focus on the development of a specific defensive strategy to deal with specific, often singular threats. In this paper, we address the issue of coalitional attacks, which can be launched by multiple adversaries cooperatively against the smart-world system such as smart cities. Particularly, we propose a game-theory based model to capture the interaction among multiple adversaries, and quantify the capacity of the defender based on the extended Iterated Public Goods Game (IPGG) model. In the formalized game model, in each round of the attack, a participant can either cooperate by participating in the coalitional attack, or defect by standing aside. In our work, we consider the generic defensive strategy that has a probability to detect the coalitional attack. When the coalitional attack is detected, all participating adversaries are penalized. The expected payoff of each participant is derived through the equalizer strategy that provides participants with competitive benefits. The multiple adversaries with the collusive strategy are also considered. Via a combination of theoretical analysis and experimentation, our results show that no matter which strategies the adversaries choose (random strategy, win-stay-lose-shift strategy, or even the adaptive equalizer strategy), our formalized game model is capable of enabling the defender to greatly reduce the maximum value of the expected average payoff to the adversaries via provisioning sufficient defensive resources, which is reflected by setting a proper penalty factor against the adversaries. In addition, we extend our game model and analyze the extortion strategy, which can enable one participant to obtain more payoff by extorting his/her opponents. The evaluation results show that the defender can combat this strategy by encouraging competition among the adversaries, and significantly suppress the total payoff of the adversaries via setting the proper penalty factor.
各种各样的网络攻击对由物联网（IoT）支持的智能世界系统（如智慧城市、智能电网、智能交通等）的运行构成了挑战，在智能世界系统得以全面广泛采用之前，必须谨慎且周全地应对这些攻击。尽管已经开展了许多研究工作来防范这些威胁，但大多数现有方案都专注于开发特定的防御策略以应对特定的、通常较为单一的威胁。在本文中，我们探讨了联合攻击问题，多个对手可能会协同对智慧城市等智能世界系统发动此类攻击。具体而言，我们提出了一种基于博弈论的模型来捕捉多个对手之间的互动，并基于扩展的迭代公共物品博弈（IPGG）模型量化防御者的能力。在形式化的博弈模型中，在每一轮攻击中，参与者既可以通过参与联合攻击进行合作，也可以选择置身事外进行背叛。在我们的工作中，我们考虑了具有检测联合攻击概率的通用防御策略。当检测到联合攻击时，所有参与攻击的对手都会受到惩罚。每个参与者的预期收益是通过均衡策略推导出来的，该策略为参与者提供了竞争优势。我们还考虑了采用勾结策略的多个对手。通过理论分析和实验相结合，我们的结果表明，无论对手选择何种策略（随机策略、赢留输变策略，甚至是自适应均衡策略），我们的形式化博弈模型都能够通过提供足够的防御资源，使防御者大幅降低对手预期平均收益的最大值，这通过对对手设置适当的惩罚因子得以体现。此外，我们扩展了博弈模型并分析了敲诈策略，该策略能使一个参与者通过敲诈对手获得更多收益。评估结果表明，防御者可以通过鼓励对手之间的竞争来对抗这种策略，并通过设置适当的惩罚因子显著抑制对手的总收益。
