Research and implementation of network intrusion detection technology based on deep learning

With the continuous development of network technology, network intrusion and data leakage have become one of the most important challenges in the field of Internet security. Traditional network security technology often relies on rules and signatures, but this approach cannot keep up with increasingly sophisticated attack techniques. Therefore, in the increasingly complex Internet environment, network intrusion detection technology based on deep learning has become a new trend in the field of modern network security.

This article will introduce the research and implementation of network intrusion detection technology based on deep learning technology. This article will focus on introducing the basic knowledge of deep learning and network intrusion detection methods, expounding the application of deep learning in network intrusion detection, and specifically implementing a network intrusion detection model based on deep learning. Finally, the implementation of network intrusion detection technology based on deep learning will be discussed. The advantages and disadvantages are discussed.

1. Introduction to the basic knowledge of deep learning

Deep learning is a type of machine learning that imitates the working way of the human brain's neural network. Deep learning uses multi-layered neural networks to learn and solve complex problems. Simple neurons form a multi-layer neural network, and each neuron calculates its output through weights and biases.

The most commonly used neural network architectures in deep learning include convolutional neural networks (CNN) and long short-term memory networks (LSTM). Convolutional neural networks are suitable for image recognition, while LSTM is suitable for language processing. These networks can be trained to learn a mapping from input to output, updating weights on each neuron in the network.

2. Network intrusion detection technology

Network intrusion detection technology refers to the use of specific algorithms and tools to detect network traffic to discover possible security vulnerabilities or malicious behaviors. Common network intrusion detection methods include rule-based, statistics-based and machine learning-based.

Traditional detection methods based on rules and statistics can handle known attacks well, but it is difficult to deal with unknown and changing attack methods, while network intrusion detection methods based on machine learning can learn Patterns in data to identify new attacks.

3. Network intrusion detection technology based on deep learning

Network intrusion detection technology based on deep learning is a network intrusion detection method based on machine learning. This method mainly detects abnormal behaviors in network traffic by training deep neural networks to quickly identify network intrusion behaviors.

In deep learning methods, convolutional neural networks (CNN) and long short-term memory networks (LSTM) can be used to process network data. Convolutional neural networks can learn the spatial and temporal characteristics of network data, improving the accuracy of network intrusion detection. LSTM can learn the time series characteristics of network data to further improve the effect of network intrusion detection methods based on deep learning.

4. Implementation of network intrusion detection based on deep learning

This article implements a network intrusion detection model based on deep learning, using the KDD’99 data set for training and testing. The specific implementation process is as follows:

1. Data preprocessing

The KDD'99 data set contains five sub-data sets: training set, test set, training attack set, and test attack set and small data sets. This article uses the training set and test set for model training and testing. The data set contains 42 features, which require data preprocessing. First remove duplicate records, then encode non-numeric features and standardize the features.

2. Training model

This article uses convolutional neural network (CNN) and long short-term memory network (LSTM) for model training. Developed using the Keras deep learning framework in Python.

First use the convolutional neural network to process the network intrusion data, and then use the long short-term memory network to process the time series characteristics of the network data. During the model training process, cross-validation techniques are used to evaluate the training effect.

3. Test the model

Use the test set to test the model and evaluate the accuracy and efficiency of the model. Use the test data set to evaluate the model's performance and perform analysis.

5. Analysis of the advantages and disadvantages of network intrusion detection technology based on deep learning

Network intrusion detection technology based on deep learning has the following advantages:

1. Can handle unknowns and changing attack patterns.
2. Can improve the accuracy of network intrusion detection.
3. Can reduce the false alarm rate.

However, network intrusion detection technology based on deep learning also has the following shortcomings:

1. Requires large computing resources.
2. The requirements for training data are relatively high.
3. The model has poor interpretability.

6. Summary

This article introduces the research and implementation of network intrusion detection technology based on deep learning technology, and analyzes the advantages and disadvantages of network intrusion detection technology based on deep learning. analyze. This paper proposes a method for network intrusion detection using convolutional neural networks and long short-term memory networks, and implements it on the KDD'99 data set. Network intrusion detection based on deep learning technology will become an important research direction in the field of network security in the future.

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