Analysis assaulting pattern for the security problem monitoring in 5G-enabled sensor network systems with big data environment using artificial intelligence/machine learning

Abstract

The 5G-enabled sensor network systems make it possible to connect cyber and real ‘things’ in many ways. Even so, the flow of data between 5G-enabled sensor devices brings big data environment problems, such as huge amounts of data, duplicated data, a lack of scalability etc. Some of these problems are as follows. It is hard to keep an eye on 5G-enabled sensor systems in a ‘big data’ environment. Cyberattacks that could put the safety of the sensor network systems at risk are hard to find, which make the situation even more complicated. The security challenges of 5G-enabled Sensor Network Systems are studied and analyzed due to some constraints associated with the sensor nodes. The proposed advanced algorithm for securing the 5G-enabled sensor systems is a Multidimensional big data environment using artificial intelligence/machine learning (AI/ML). Using a structure that depends on both geographical and temporal data, an improved clear point selection operation may get important information from multidimensional time series data that is spread across a wide range of sensor nodes. Therefore, the actions of the 5G-enabled sensor network can be shown accurately and a complete model of its underlying data structure is built to analysis attacking, pattern on 5G-enabled Sensor Network Systems using the AI/ML Algorithm.