In recent years, industrial control systems have evolved toward stability and efficiency, increasing industrial control systems interconnected with the Internet, which means that industrial control systems are facing more serious cyber threats. Thus, it is critical for enterprises to consider issues related to data privacy and network security. Digital twin enables real-time synchronization and simulation of data from various physical components of industrial control systems. However, anomaly detection of twin data is still challenging because existing methods are usually multi-stage with tedious training and detection steps. Therefore, we propose a method called end-to-end anomaly detection with the aim to accomplish real-time anomaly detection quickly and accurately. In order to seek key features, multidimensional deconvolutional network and attention mechanism are applied to our model. The results of this study indicate that our method performs well on precision and F1 score in comparison to the state-of-art methods.

As information technology continues to evolve, cloud data centres have become increasingly prominent as the preferred infrastructure for data storage and processing. However, this shift has introduced a new array of security challenges, necessitating innovative approaches distinct from traditional network security architectures. In response, the Zero Trust Architecture (ZTA) has emerged as a promising solution, with micro-segmentation identified as a crucial component for enabling continuous auditing and stringent security controls. VxLAN technology is widely utilized in data centres for tenant isolation and virtual machine interconnection within tenant environments. Despite its prevalent use, limited research has focused on its application in micro-segmentation scenarios. To address this gap, we propose a method that leverages VLAN and VxLAN many-to-one mapping, requiring that all internal data centre traffic routes through the VxLAN gateway. This method can be implemented cost-effectively, without necessitating business modifications or causing service disruptions, thereby overcoming the challenges associated with micro-segmentation deployment. Importantly, this approach is based on standard public protocols, making it independent of specific product brands and enabling a network-centric framework that avoids software compatibility issues. To assess the effectiveness of our micro-segmentation approach, we provide a comprehensive evaluation that includes network aggregation and traffic visualization. Building on the implementation of micro-segmentation, we also introduce an enhanced asset behaviour algorithm. This algorithm constructs behavioural profiles based on the historical traffic of internal network assets, enabling the rapid identification of abnormal behaviours and facilitating timely defensive actions. Empirical results demonstrate that our algorithm is highly effective in detecting anomalous behaviour in intranet assets, making it a powerful tool for enhancing security in cloud data centres. In summary, the proposed approach offers a robust and efficient solution to the challenges of micro-segmentation in cloud data centres, contributing to the advancement of secure and reliable cloud infrastructure.

Truth discovery endeavors to extract valuable information from multi-source data through weighted aggregation. Some studies have integrated differential privacy techniques into traditional truth discovery algorithms to protect data privacy. However, due to the neglect of outliers and limitations in budget allocation, these schemes still need improvement in the accuracy of discovery results. To solve these challenges, we propose a privacy-preserving scheme called PriPTD to achieve secure and accurate truth discovery services over crowdsourced data streams. Instead of assuming that worker weights are always stable between two neighboring timestamps, we delve deeper to consider outliers where worker weights change rapidly. Accordingly, we develop an outlier-aware weight estimation method with a time series model to capture and handle these outliers. Furthermore, to ensure data utility under a limited budget, we devise a weight-aware budget allocation algorithm. Its core idea is that timestamps with higher importance consume a larger proportion of the remaining budget. Additionally, we design a noise-aware error adjustment approach to mitigate the adverse effects of introduced noise on accuracy. Theoretical analysis and extensive experiments validate our scheme. Final comparative experiments against existing works confirm that our scheme achieves more accurate truth discovery while preserving privacy.

In IoT-driven intelligent perception systems, multi-modal crowd counting using visible-thermal (RGB-T) sensor arrays deployed on edge nodes plays a vital role in urban management. Existing methods primarily focus on inter-modal feature fusion but suffer from noise amplification when directly integrating thermal features with degraded visible images under low-light conditions, leading to inaccurate density estimation and poor nighttime performance. To address this, we propose the first domain-adaptive network for RGB-T crowd counting. First, we introduce a dark-light domain adaptation module based on Retinex decomposition. Unlike traditional approaches that extract only bright and thermal domain features, our module employs a dual decomposition-recomposition process to learn illumination-invariant features from RGB images, enhancing discriminability in dark regions while preserving bright-domain semantics. Second, the module is supervised by feature alignment, reflectance consistency, and decomposition invariance losses, strengthening its robustness under extreme illumination degradation. Finally, a multi-domain progressive decision fusion module is designed to leverage shared and domain-specific features through public feature extraction, dual-domain fusion, and weighted decision processes, improving generalization across varying lighting conditions. Trained solely on well-lit data, our method generalizes effectively to dark scenarios. Experiments on DroneRGBT and RGBTCC datasets demonstrate superior performance over state-of-the-art fusion methods, particularly excelling in low-light crowd counting.