Online social networks (OSNs) facilitate the rapid and extensive spreading of rumors. While most existing methods for debunking rumors consider a solitary debunker, they overlook that rumor-mongering and debunking are interdependent and confrontational behaviors. In reality, a debunker must consider the impact of rumor-mongering behavior when making decisions. Moreover, a single rumor-debunking strategy is ineffective in addressing the complexity of the rumor environment in networks. Therefore, this article proposes a hybrid rumor-debunking approach that combines truth dissemination and regulatory measures based on the differential game theory under adversarial behaviors of rumor-mongering and debunking. Toward this end, we first establish a rumor propagation model using node-based modeling techniques that can be applied to any network structure. Next, we mathematically describe and analyze the processes of rumor-mongering and debunking. Finally, we validate the theoretical results of the proposed method through various comparative experiments, including comparisons with a random strategy, a uniform strategy, and single strategy models on real-world datasets collected from Facebook, Twitter, and YouTube. Furthermore, we harness two actual rumor events to estimate parameters and predict rumor propagation, thereby affirming the veracity and effectiveness of our rumor propagation model.

Industrial Internet of Things (IIoT) is vulnerable to advanced persistent threat (APT). In this article, we study a scenario in which APT is launched to attack IIoT devices. Considering the APTs lateral movement, a node-level state evolution model is established to calculate the probability of every device in an IIoT system to be compromised by APT. Based on this, a Stackelberg game model is proposed for the APT attacker and defender, which can accurately describe the gaming process. An effective computational approach is developed to obtain the potential Stackelberg equilibrium strategy pair of the game. Extensive case studies and comparison studies are conducted to validate the effectiveness of the proposed method.

Backboned by smart meter networks, Advanced Metering Infrastructures (AMIs) play a critical role in smart grids. This paper studies a new False Data Injection Attack (FDIA) scenario targeting AMIs, in which the attacker injects and propagates computer worms (i.e., false data codes) to maliciously increase the readings of networked smart meters and create economic loss to the end customers. This paper establishes the false data code propagation and attack models in such a scenario; based on this, this paper proposes a differential game model for describing the attack and defense process for FDIA against AMIs. A computationally efficient algorithm is developed to solve the proposed differential model and obtain the potential Nash equilibrium (NE) strategy pair. Extensive numerical simulations are conducted to validate the effectiveness of the proposed method under different energy tariff structures.

Deploying Intrusion Detection Systems (IDSs) is an essential way to enhance the security of Multi-hop Clustered Wireless Sensor Networks (MCWSNs). The conventional IDS deployment architectural designs show limitations in ensuring the security of MCWSNs due to the limited monitoring range of the nodes. This paper proposes an efficient IDS deployment architecture for MCWSNs. The architecture is a hybrid design, in which the cluster heads and the sink collaboratively act as IDS agents to monitor the entire network and perform intrusion detection. Based on the new architecture, this paper proposes a resource allocation model to optimally allocate resources among the IDS agents so that the network’s security metric can be maximized. A comprehensive analytical framework is proposed to analyze the optimality of the model’s solution, and an efficient computational approach is developed to obtain the optimal resource allocation strategy. Extensive numerical simulation and comparison studies are conducted to validate the proposed method.