# A Survey: Network Feature Measurement Based on Machine Learning
###### Abstract  
In network management, network measuring is crucial. Accurate network measurements can increase network utilization, network management, and the ability to find network problems promptly. With extensive technological advancements, the difficulty for network measurement is not just the growth in users and traffic but also the increasingly difficult technical problems brought on by the network's design becoming more complicated. In recent years, network feature measurement issues have been extensively solved by the use of ML approaches, which are ideally suited to thorough data analysis and the investigation of complicated network behavior. However, there is yet no favored learning model that can best address the network measurement issue. The problems that ML applications in the field of network measurement must overcome are discussed in this study, along with an analysis of the current characteristics of ML algorithms in network measurement. Finally, network measurement techniques that have been used as ML techniques are examined, and potential advancements in the field are explored and examined. network measurement; machine learning; latency; packet loss; throughput; bandwidth; congestion control; path loss +
Footnote †: journal: Medical  
and optimization. An accurate path loss description and modeling, as well as sufficient network planning, are needed to satisfy user expectations. These issues are specifically categorized as classification and regression issues, and ML is used to create models to address serious flaws [8]. Networks have been created to connect ubiquitous physical items as a result of the advancement of modern technology. Rich neural network structures can reduce the drawbacks and negative effects of conventional IOT technologies, and as more high-quality data becomes available, neural networks can offer dependable adaptive IOT solutions [9; 10; 11]. With the success of deep learning (DL) and neural networks (NNs), intelligent data processing and analysis are driving the development of IOT applications [12]. The ML approach used by manufacturers of intelligent IOT devices is extended by AI technology, enabling them to make complicated judgments based on adaptability and to improve network management to optimize the distribution of network resources. Numerous studies have merged them due to AI's strong performance in security and network data processing, which produces trustworthy results [13]. Predicting future network behavior, achieving fair resource allocation and utilization, and measuring the network structure to dynamically describe it are the key goals of network measurement. It is conceivable to employ machine learning's maximum likelihood capabilities to effectively handle unstructured and seemingly intractable problems and to enhance network performance [14]. In other words, the discipline of network measurement can benefit from the application of machine learning. Machine learning's classification and regression capabilities can be very helpful in network intrusion detection and feature prediction. They can also automatically change parameters in particular situations and support network scheduling. Additionally, in some complex system situations, network issues must create precise models to fully account for complicated network behaviors. Right now, machine learning can be utilized to deliver an exact model that is more in line with expectations [15]. There are currently two difficulties in using ML techniques with a network measuring system. 1. To process a significant volume of complicated, high-speed, and heterogeneous network monitoring data gathered in particular applications, network measurement must first assure a real-time environment. These data frequently emerge as fast data streams. Applications for network measurement must be able to swiftly and continuously examine this data. A data analysis model that can continue to process significant amounts of data offline is preferred;
2.