State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, 710071, China.
Institute of Information Engineering Chinese Academy of Sciences, Beijing, 100093, China.
Sensors (Basel). 2020 Feb 1;20(3):804. doi: 10.3390/s20030804.
With limited computing resources and a lack of physical lines of defense, the Internet of Things (IoT) has become a focus of cyberattacks. In recent years, outbreak propagation attacks against the IoT have occurred frequently, and these attacks are often strategical. In order to detect the outbreak propagation as soon as possible, t embedded Intrusion Detection Systems (IDSs) are widely deployed in the IoT. This paper tackles the problem of outbreak detection in adversarial environment in the IoT. A dynamic scheduling strategy based on specific IDSs monitoring of IoT devices is proposed to avoid strategic attacks. Firstly, we formulate the interaction between the defender and attacker as a Stackelberg game in which the defender first chooses a set of device nodes to activate, and then the attacker selects one seed (one device node) to spread the worms. This yields an extremely complex bilevel optimization problem. Our approach is to build a modified Column Generation framework for computing the optimal strategy effectively. The optimal response of the defender's problem is expressed as mixed-integer linear programming (MILPs). It is proved that the solution of the defender's optimal response is a NP-hard problem. Moreover, the optimal response of defenders is improved by an approximate algorithm--a greedy algorithm. Finally, the proposed scheme is tested on some randomly generated instances. The experimental results show that the scheme is effective for monitoring optimal scheduling.
由于计算资源有限且缺乏物理防线，物联网 (IoT) 已成为网络攻击的焦点。近年来，针对物联网的爆发式传播攻击频繁发生，而且这些攻击往往具有策略性。为了尽快检测到爆发式传播，嵌入式入侵检测系统 (IDS) 被广泛部署在物联网中。本文针对物联网中的对抗环境下的爆发检测问题进行了研究。提出了一种基于特定 IDS 对物联网设备进行监控的动态调度策略，以避免策略性攻击。首先，我们将防御者和攻击者之间的相互作用建模为一个 Stackelberg 博弈，其中防御者首先选择一组设备节点进行激活，然后攻击者选择一个种子（一个设备节点）来传播蠕虫。这产生了一个极其复杂的双层优化问题。我们的方法是构建一个改进的列生成框架来有效地计算最优策略。防御者问题的最优响应表示为混合整数线性规划 (MILPs)。证明了防御者最优响应的解是一个 NP 难问题。此外，通过一种近似算法——贪婪算法来改进防御者的最优响应。最后，在所生成的实例上对所提出的方案进行了测试。实验结果表明，该方案对于监测最优调度是有效的。
