Machine learning can be used in various cybersecurity applications to provide analytical approaches to detect and react to an attack. Additionally, it can improve security procedures.
Some typical machine learning mechanisms used in network traffic analysis include:
1.Network-based defense: This technique tries to lower the likelihood of attacks on the network by adding an extra layer of defense. It does this by setting up policies and controls to specify users who are authorized to do what on the network.
2.Intrusion detection: Applications can use machine learning algorithms to detect cyberattacks and take appropriate action before they go into effect. This is done by using network surveillance software to look for malicious activity where the behavior varies from normal users.
There are two main problems with using machine learning for network traffic analysis:
Time and requirements: The model must be trained and updated on a regular basis in order to classify network traffic effectively. This process can be time-consuming, especially when dealing with large amounts of data. Additionally, the model must be specific to the task it is being used for.
Complexity: The process of classifying network traffic is complex due to the ever-changing nature of cyberattacks. There are many factors to consider when determining how well-suited a particular method is for classifying a particular type of attack. Additionally, each technical layer has its own subcategories,
which makes it difficult to use the same algorithms across them.
Overall, machine learning is a powerful tool that can be used to improve cybersecurity. However, it is important to be aware of the limitations of this technology and to take steps to mitigate these limitations.
Reference paper: https://www.sciencedirect.com/science/article/pii/S1877050920305494%5C
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