A review of deep learning based intrusion detection systems
DOI:
https://doi.org/10.54097/hset.v56i.10104Keywords:
IDS, deep learning, intrusion detectionAbstract
Network security is a key issue in the era of rapid Internet development and new technologies are needed to enhance the security protection of network systems. As the first line of defence for the security of network systems, IDS is considered to be one of the important network tools for managing network security. Traditional network intrusion detection methods usually use machine learning techniques. However, deep learning, a more powerful machine learning method, has the advantages of mature technology, wide applicability and high accuracy, and has shown its advantages in various fields such as image recognition, natural language processing and computer vision. It can also be applied to network security detection, as it can effectively handle large-scale, complex datas and detect unknown, sophisticated network attacks. This paper will provide a summary of machine learning techniques and deep learning techniques in intrusion detection systems, as well as examples of IDSs using deep learning, and conclude with a summary and outlook.
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