## 1 Introduction  
The transition of the industrial network infrastructure in the era of Industry 4.0 requires that diverse communication requirements are fulfilled, including those of smart cities, manufacturing technologies, and Industrial Internet of Things (IIoT) applications [1, 2]. With the advent of 5G networks, there is great potential to fulfill these requirements by offering high-speed connectivity, ultra-low latency, and massive capacity [3]. However, the heterogeneity of vertical industries introduces various application scenarios such as healthcare, smart grids, and intelligent transportation, each with diverse business characteristics and network demands. These differences pose challenges for the successful implementation and optimization of 5G networks in industrial environments [4, 5]. As a result, optimizing 5G networks for industrial settings requires tailored approaches that consider the specific requirements of each vertical sector. Efficient resource allocation, traffic management, and quality of service (QoS) provisioning become critical factors that need to be addressed to maximize the benefits of 5G technology in industrial applications [6]. Developing optimization algorithms that can account for the interplay between the network infrastructure, industrial applications, and dynamic workloads is essential for achieving optimal network performance and meeting the unique demands of different industrial verticals. Future research efforts should focus on developing innovative solutions for network monitoring and optimization in industrial environments [7]. This includes exploring techniques for data labeling and traffic analysis in industrial networks, as well as designing adaptive and dynamic optimization algorithms that can adapt to the changing needs of industrial environments. The large-scale connectivity of IoT devices in 5G vertical applications introduces additional complexity to network monitoring. The sheer number of connected devices, each generating data and contributing to the overall network traffic, presents a challenge in effectively managing and analyzing the vast amount of information [8]. The diversity and differentiation of 5G services and terminals further complicate the monitoring process. Each vertical industry may have unique requirements and network demands, necessitating tailored monitoring approaches to ensure their optimal performance and meet specific needs [9]. Moreover, the dynamic nature of 5G networks and the real-time requirements of certain vertical industries pose further challenges [10]. For instance, industries such as intelligent transportation or healthcare demand real-time monitoring to enable timely decision making and ensure the safety and efficiency of operations. Meeting these high real-time requirements and providing actionable insights in a timely manner presents a significant technical challenge for network monitoring in 5G vertical applications. However, these challenges also present opportunities for advancements in network monitoring techniques. Innovative approaches such as machine learning (ML), artificial intelligence (AI), and data analytics can be leveraged to analyze unlabeled network data and effectively detect anomalies [11]. By harnessing the power of these technologies, operators can gain valuable insights into network performance, detect potential issues in advance, and take proactive measures to optimize network resources and ensure reliable and efficient operations. In the realm of anomaly detection for time series network data, the identification of unusual behavior within a sequence of interconnected data is of the utmost importance. However, one of the major challenges faced in this field is the lack of anomaly labeling, making supervised learning approaches infeasible. To tackle this challenge, researchers have developed various unsupervised techniques in recent years. These techniques aim to detect anomalies without relying on labeled examples.