Machine Learning For 5G Security Using Random Forest
Gayatri S. Chavhan, Asakti Rautkar, Jitendra Prithviraj, Rahul Agrawal, Nekita Chavhan, Chetan Dhule
Abstract
The abstract for the research paper on "Machine Learning for 5G Security using Random Forest" discusses the importance of 5G security in the current digital age and the role of machine learning in addressing security concerns. The capacity of machine learning algorithms to assess and identify security threats in real-time has led to a rise in their appeal in the field of cybersecurity. With the emergence of 5G networks, there is an increasing need for robust security measures to protect the network from various types of attacks. In this context, the use of Random Forest (RF) as a machine learning algorithm has shown promising results in detecting security threats in 5G networks. This study provides an overview of the use of RF in 5G security while emphasizing its benefits and drawbacks. The study includes a detailed discussion of the working principle of RF, its suitability for security analysis, and the various metrics used to evaluate its performance. The paper proposes the use of Random Forest algorithm, a powerful machine learning technique, for securing 5G networks. The research explores the application of Random Forest to identify potential security threats and classify network traffic into secure and insecure categories. The suggested approach demonstrates great accuracy in identifying security concerns when tested using data from a real-world 5G network. The outcomes show how machine learning approaches can improve 5G security and offer a dependable line of defence against cyber attacks. The paper concludes by highlighting the significance of this work and its potential for further research in the field of 5G security.