Machine Learning to Enhance Network Security

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Abstract
Network security is necessary to shield computer systems and data from unauthorized access, breaches, and cyberattacks. Machine learning can help make network security with more effective threat detection and response. Because machine learning techniques are intelligent and flexible, they are powerful tools to improve network security. An outline of the main uses of machine learning in network security is given in this abstract, with special attention on how ML is used in threat detection, anomaly identification, and incident response. After being trained on enormous datasets, machine learning algorithms can recognize patterns in typical network behaviours and distinguish variations that could be signs of security risks. Machine learning is a proactive security against cyber threats that includes features like behavioral profiling, predictive analysis, and intrusion detection. This chapter highlights the value of machine learning in enhancing conventional security measures and provides insights into its uses in a range of network security contexts for a resilient and flexible network. Request access from your librarian to read this chapter's full text.

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