Research on intranet intrusion detection technology based on deep learning

As network attacks become increasingly complex and concealed, intranet security issues have also attracted increasing attention. Intranet intrusion detection technology is an important means to ensure corporate network security. Traditional intrusion detection technology mainly relies on traditional means such as rule libraries and feature libraries. However, this method has problems such as high missed detection rate and high false positive rate. Intranet intrusion detection technology based on deep learning has become an important way to solve these problems.

Deep learning is an emerging branch of artificial intelligence. It uses the human brain neural network as a model and achieves high-accuracy prediction and classification capabilities through learning iterations of large amounts of data. Deep learning is widely used in image, voice and other fields, and is increasingly used in the field of network security.

Intranet intrusion detection technology based on deep learning has the following advantages compared with traditional methods:

1. Strong adaptability: In view of the rapid update of network attack methods, traditional methods The rule base and feature base need to be continuously maintained and updated, and deep learning-based technology can adaptively adjust the model by learning large amounts of data to better discover and deal with various network security threats.
2. Good robustness: Traditional methods are not very tolerant of changes by attackers. Once the attacker changes in attack methods, traditional methods may miss detection, while deep learning-based technology can learn Detection is based on the characteristics of the data and is relatively more tolerant of changes by attackers.
3. High accuracy: Deep learning-based technology can find the best model through iterative learning, thereby improving detection accuracy.

In specific practice, intranet intrusion detection technology based on deep learning is mainly divided into several steps such as data preprocessing, feature extraction, feature conversion and classification prediction. Among them, data preprocessing mainly involves operations such as cleaning, extreme value processing and normalization of data to ensure the quality and standardization of data; feature extraction is to transform raw data into quantifiable feature vectors that can be processed by machine learning algorithms. , these feature vectors usually contain a large amount of statistical information, frequency domain information, time domain information, etc.; feature conversion is to process the feature vectors and perform operations such as comparison, filtering and merging to facilitate prediction by the machine learning model; classification prediction is through Machine learning models perform classification predictions to distinguish abnormal data from normal data.

It is worth noting that intranet intrusion detection technology based on deep learning is still in the development stage and faces many challenges. The biggest challenge is that it is difficult for deep learning algorithms to achieve good performance when there is insufficient data. Therefore, when applying deep learning-based intranet intrusion detection technology, the quality and diversity of data are very important.

To sum up, intranet intrusion detection technology based on deep learning is a new technology with application potential. With the increase in various types of network attack methods, deep learning-based technology will play an increasingly important role in the field of intranet security. More research and practice will further promote the development and popularization of this technology.

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