Crowdsensing systems collect various types of data from sensors embedded on mobile devices owned by individuals. These individuals are commonly referred to as workers that complete tasks published by crowdsensing systems. Because of the relative lack of control over worker identities, crowdsensing systems are susceptible to data poisoning attacks which interfering with data analysis results by injecting fake data conflicting with ground truth. Frameworks like TruthFinder can resolve data conflicts by evaluating the trustworthiness of the data providers. These frameworks somehow make crowdsensing systems more robust since they can limit the impact of dirty data by reducing the value of unreliable workers. However, previous work has shown that TruthFinder may also be affected by data poisoning attack when the malicious workers have access to global information. In this paper, we focus on partially observable data poisoning attacks in crowdsensing systems. We show that even if the malicious workers only have access to local information, they can find effective data poisoning attack strategies to interfere with crowd sensing systems with TruthFinder. First, we formally model the problem of partially observable data poisoning attack against crowdsensing systems. Then, we propose a data poisoning attack method based on deep reinforcement learning, which helps malicious workers jeopardize with TruthFinder while hiding themselves. Based on the method, the malicious workers can learn from their attack attempts and evolve the poisoning strategies continuously. Finally, we conduct experiments on real-life data sets to verify the effectiveness of the proposed method.

In softwarized industrial networking, honeypot identification is very important for both the attacker and the defender. Existing honeypot identification relies on simple features of honeypot. There exist two challenges: The simple feature is easily simulated, which causes inaccurate results, whereas the advanced feature relies on high interactions, which lead to security risks. To cope with these challenges, in this article, we propose a secure fuzzy testing approach for honeypot identification inspired by vulnerability mining. It utilizes error handling to distinguish honeypots and real devices. Specifically, we adopt a novel identification architecture with two steps. First, a multiobject fuzzy testing is proposed. It adopts mutation rules and security rules to generate effective and secure probe packets. Then, these probe packets are used for scanning and identification. Experiments show that the fuzzy testing is effective and corresponding probe packet can acquire more features than other packets. These features are helpful for honeypot identification.

There is a large amount of real-time data, e.g., measurement data and instructions, among controllers, sensors, and actuators in the Industrial IoT. These data are vulnerable to unauthorized access and tampering. In addition, once the controller is controlled by malicious code, it may send out dangerous instruction that is not compliant with the preset control process, which seriously interferes with the industrial control process. To achieve correct and undisturbed control based on real-time data, we propose an attribute-based access control (ABAC) method for real-time data in the Industrial IoT to mitigate unauthorized access and tampering, and noncompliant operation. First, we analyze the abnormal behaviors of real-time data interaction in the Industrial IoT. Second, we propose the multilevel hash identity authentication method to identify and block unauthorized access and tampering with real-time data. And, then we model the timing relationship and task logic relationship of the control process into the attribute fields of the ABAC method to identify and block noncompliant operations. Further, we design an access control module and display the lightweight deployment under the availability constraints of the control service. Finally, the proposed access control method is analyzed, proved, and experimented with. The results show that the proposed method can prevent unauthorized and noncompliant behaviors to field real-time data, meanwhile, it has a controllable delay and better scalability.

Embodied AI systems, integrating Large Vision-Language Models (LVLMs) and Large Language Models (LLMs) with physical actuators and sensors, face unique robustness and security challenges stemming from the complex interplay between perception, cognition, and actuation in real-world environments. This survey provides a systematic analysis of these vulnerabilities and associated attack surfaces. We propose a tripartite vulnerability taxonomy comprising foundational, integration, and contextual risks. Foundational vulnerabilities arise from inherent limitations in current AI architectures and training paradigms; Integration vulnerabilities emerge from the composition of cyber-physical components; And contextual vulnerabilities stem from dynamic physical environments and deployment conditions. Correspondingly, we present a comprehensive attack taxonomy that encompasses foundational attacks on LLMs/LVLMs (including logits-based, optimization-based, prompt-based, and cross-modality attacks), integration-level cybersecurity threats (such as man-in-the-middle, firmware, side-channel, and supply chain attacks), and contextual attacks (primarily sensor spoofing across multiple modalities). We further examine representative failure modes of the cognitive core, review existing evaluation methodologies and benchmarks, and synthesize a multi-layer defense framework that integrates perceptual redundancy, runtime monitoring, and hardware-enforced safety mechanisms. This work offers a unified conceptual framework and practical roadmap for designing and evaluating robust, secure Embodied AI systems in safety-critical real-world deployments.

With the continuous integration of information technology and operation technology, the Industrial Internet of Things (IIoT) is gradually changing from closed to open. Operators can configure, monitor, or control the industrial production process remotely via Internet, which brings security threats to IIoTs. Since the IIoT focuses on the availability of industrial production, it is unfeasible to study security issues directly on the industrial field. Thus, constructing an IIoT cyber range to reproduce industrial scenarios for offensive and defensive confrontation research is necessary. However, the traditional IIoT cyber range relies on physical industrial field devices that are not reproducible and hard to recover from cyber-attacks. To solve these problems, in this article, we propose a framework for a digital twin-based cyber range and a digital twin construction method with multiple models. Cyber ranges with digital twins are more flexible and convenient. Based on the proposed method, an industrial scenario is reproduced using machine learning algorithms to predict temperature changes from different perspectives. The experimental result shows the ability of digital twins to construct an IIoT cyber range to reproduce production processes and replace field devices.

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.