The Application of Machine Learning in the Field of Network Security

Abstract

Nowadays, as the technology of AI is becoming more and applying in more fields, there rises some doubt about the safety of using AI. This paper explores the application of machine learning in the field of network security. The introduction provides an overview of machine learning and its importance in ensuring the security of networks. It also highlights the challenges and opportunities when implementing machine learning techniques in network security. The second section delves into the various machine learning techniques used in network security. Supervised learning is discussed concerning intrusion detection, while unsupervised learning is explored for anomaly detection. Reinforcement learning is also examined for secure routing purposes. The third section focuses on the applications of machine learning in intrusion detection. Deep learning, natural language processing, and graph-based machine learning are explored as methods for detecting intrusions. In conclusion, this paper summarizes the key findings of the research conducted. It highlights the implications for future research and provides recommendations for practitioners in the field of network security. Overall, machine learning has proven to be a valuable tool in enhancing the security of networks, but further research and development are needed to address the challenges and fully exploit its potential.