With the rapid advancement of Internet of Things and artificial intelligence, device automation systems have become increasingly integrated with physical environments, introducing new security challenges for Trigger-Action Programming. An improper configuration of TAP rules may lead to severe cascading threats. However, existing methods typically rely on predefined safety properties and fail to capture the underlying semantic dependencies and interactions among rules. To address these limitations, we propose TAPGuard, a semantics-enhanced framework for TAP rule linkage modeling and cascading threat detection. Specifically, we identify two types of cascading threats: explicit threats, which arise from direct device interactions, and implicit threats, which are induced by shared environmental variables and may propagate across semantically related but structurally disconnected rules. TAPGuard leverages large language models to extract structured semantic elements from natural language rule descriptions and incorporates a semantic alignment module to assess the functional similarity between rules. Building on this, we propose a dual-relation context encoder incorporating node-level and semantic-level attention to model heterogeneous dependencies and enable multi-hop relational reasoning in the heterogeneous TAP rule graph. We evaluate TAPGuard on a real-world smart home dataset and demonstrate its effectiveness in detecting cascading threats. Experimental results show that TAPGuard significantly outperforms state-of-the-art graph-based baselines.

Traffic congestion is becoming an increasingly prominent problem, and intelligent traffic signal control methods can effectively alleviate it. Recently, there has been a growing trend of applying reinforcement learning to traffic signal control for adaptive signal scheduling. However, most existing methods focus on improving traffic performance while neglecting the issue of scheduling fairness, resulting in long waiting time for some vehicles. Some works attempt to address fairness issues but often sacrifice transport performance. Furthermore, existing methods overlook the challenge of sample efficiency, especially when dealing with diversity-limited traffic data. Therefore, we propose a Fairess-aware and sample-Efficient traffic signal control method called FELight. Specifically, we first design a novel fairness metric and integrate it into decision process to penalize cases with high latency by setting a threshold for activating the fairness mechanism. Theoretical comparison with other fairness works proves why and when our fairness could bring advantages. Moreover, counterfactual data augmentation is employed to enrich interaction data, enhancing the sample efficiency of FELight. Self-supervised state representation is introduced to extract informative features from raw states, further improving sample efficiency. Experiments on real traffic datasets demonstrate that FELight provides relatively fairer traffic signal control without compromising performance compared to state-of-the-art approaches.

Exploring the state space efficiently is a crucial problem in reinforcement learning as it holds significant importance for learning optimal policies. One effective approach involves learning different sub-policies to cover various sub-spaces of the state space, with each sub-policy corresponding to a specific goal. However, the unevenness of the state probability distribution may lead to exploration difficulties in deep reinforcement learning. To overcome this challenge, we propose a Phase State Graph Exploration framework (PSGE), guiding the agent towards more promising directions for exploration. Specifically, we design a graph-based state space exploration framework to separate the combination space into sub-spaces and define the combination space and evaluation criteria for the agent’s sub-policies. In addition, hypernetwork is leveraged to decouple sub-policies and sub-goals, ensuring diversity among the agent’s sub-policies and reward shaping is used to provide dense internal reward signals for policy training, which encourages the agent to learn more efficiently. Experiments on combining control and navigation tasks demonstrate that PSGE performs well in controlling agent across various difficulty level tasks.

The rapid expansion of the Internet of Things (IoT) has led to the development of smart homes and automation systems. Trigger-action programming (TAP) has emerged as a prevalent paradigm used in IoT, facilitating the creation of automation rules. However, with the proliferation of TAP rules, the potential for conflicts between them grows significantly, which results in undesired outcomes or even safety risks. In this article, we propose a context-aware conflict detection framework for TAP rules, called CCDF-TAP, to identify the potential rule conflicts. Specifically, we incorporate external knowledge and context information during the TAP data preprocessing stage, which is conducive to accurately defining the rule conflicts. Then, based on the above information, the conflict types are defined and a conflict graph is constructed, which establishes a unified format for the rule conflict detection task. Finally, we propose a novel algorithm called dual-channel graph attention auto-encoders (DualGAAs) for efficient conflict detection, which takes the conflict graph as the input and excels in accurately identifying conflicts. Extensive experiments conducted on a comprehensive IFTTT data set demonstrate the superiority of DualGAA in detecting conflicts, achieving an exceptional accuracy of 98.85% and an F1 score of 98.91%. The contributions of our study offer a comprehensive end-to-end solution for context-aware conflict detection in TAP rules, thereby significantly enhancing the security and dependability of IoT smart home systems.

Traffic congestion is becoming an increasingly prominent problem, and intelligent traffic signal control methods can effectively alleviate it. Recently, there has been a growing trend of applying reinforcement learning to traffic signal control for adaptive signal scheduling. However, most existing methods focus on improving traffic performance while neglecting the issue of scheduling fairness, resulting in long waiting time for some vehicles. Some works attempt to address fairness issues but often sacrifice transport performance. Furthermore, existing methods overlook the challenge of sample efficiency, especially when dealing with diversity-limited traffic data. Therefore, we propose aFairness-aware and sample-Efficient traffic signal control method called FELight. Specifically, we first design a novel fairness metric and integrate it into decision process to penalize cases with high latency by setting a threshold for activating the fairness mechanism. Theoretical comparison with other fairness works proves why and when our fairness could bring advantages. Moreover, counterfactual data augmentation is employed to enrich interaction data, enhancing the sample efficiency of FELight. Self-supervised state representation is introduced to extract informative features from raw states, further improving sample efficiency. Experiments on real traffic datasets demonstrate that FELight provides relatively fairer traffic signal control without compromising performance compared to state-of-the-art approaches. Our codes are available athttps://github.com/dxnbbsw/FELight.

As one of the most promising networks, the space–air–ground–aqua integrated network (SAGAIN) has the characteristics of wide coverage and large information capacity, which can meet various requests from users in different domains. With the rapid growth of data and information generated by the Internet of Things (IoT), SAGAIN has received much attention in recent years. However, the existing network architectures are not capable of providing personalized network services according to different task types in SAGAIN. Besides, they cannot deal with many problems in SAGAIN well, such as heterogeneous network disconnection, high network delay, intermittent interruption, and unbalanced network load. In this article, in order to solve the abovementioned problems, we propose a novel architecture for SAGAIN named task-oriented intelligent networking architecture (TOINA). First, we apply the edge-cloud computing technology and network domain division in TOINA to realize intelligent networking and reduce the latency. Second, the task-oriented networking method is proposed to provide personalized network services and increase network intelligence. Third, we intend to leverage the information center network (ICN) paradigm to build the SAGAIN and optimize the content naming rules. Furthermore, a preprocessing layer was added in the network protocol stack to perform the heterogeneous network convergence in SAGAIN. In addition, some security technologies related to network architecture are considered in SAGAIN. This article presents the background, rationale, and benefits of the TOINA for SAGAIN. Besides, a specific case is studied to illustrate the network architecture work process further.