Implications of Network Security in 5G Network and Detection of Cyber Attacks

Muhammad Imran Ghafoor, Mustafa Shakir, Shafi Ullah, Syed Aurangzeb, Mehmood Baryalai, Shariqa Fakhar, Abdul Wahid


In recent years, cellular technology has grown in popularity, increasing the importance of 5G networks. Security standards are losing trust as large attacks and concerns about privacy increase. A number of fundamental security issues are addressed in this paper. Major telecommunications firms and academic studies provide this information. Many intrusion detection algorithms, tactics, and methods are applied to detect these attacks. As a result, machine learning is in its infancy. On the basis of data classification, machine learning can also make predictions about the future. Machine learning models are utilized to illustrate an intelligent intrusion detection system. A hybrid or advanced detection system cannot use the preceding approaches' detection algorithms. Using an open-source website, machine learning algorithms are trained and then automatically applied to new data. This investigation also examined machine learning techniques. A comparison of classifier accuracy is also conducted. Using K-Means clustering to core features of the UNSW-NB15NB15 and NSLKDD datasets, the ENN model performs well. Despite lower accuracy with a single hot encoding. With a precision of 99.7% on UNSWB and 99.72% on NSLKDD, the model correctly detects assaults in the 5G Network-based dataset.

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