Network Intrusion Detection Technology Research Articles

Efficient Detection of Malicious Traffic Using a Decision Tree-Based Proximal Policy Optimisation Algorithm: A Deep Reinforcement Learning Malicious Traffic Detection Model Incorporating Entropy

With the popularity of the Internet and the increase in the level of information technology, cyber attacks have become an increasingly serious problem. They pose a great threat to the security of individuals, enterprises, and the state. This has made network intrusion detection technology critically important. In this paper, a malicious traffic detection model is constructed based on a decision tree classifier of entropy and a proximal policy optimisation algorithm (PPO) of deep reinforcement learning. Firstly, the decision tree idea in machine learning is used to make a preliminary classification judgement on the dataset based on the information entropy. The importance score of each feature in the classification work is calculated and the features with lower contributions are removed. Then, it is handed over to the PPO algorithm model for detection. An entropy regularity term is introduced in the process of the PPO algorithm update. Finally, the deep reinforcement learning algorithm is used to continuously train and update the parameters during the detection process, and finally, the detection model with higher accuracy is obtained. Experiments show that the binary classification accuracy of the malicious traffic detection model based on the deep reinforcement learning PPO algorithm can reach 99.17% under the CIC-IDS2017 dataset used in this paper.

A Network Intrusion Detection Model Based on GA-Improved NSA

With the popularization of the Internet, network security issues have also emerged. In response to network security issues, there are certain shortcomings in current network intrusion detection technologies. To improve and optimize this technology, a network intrusion detection model based on genetic algorithm and improved negative selection algorithm is designed. The generation of detectors in the selection algorithm is replaced by genetic algorithm, and the non-self spatial distribution of detectors is optimized. This paper proposes a network intrusion detection model using a genetic algorithm-improved negative selection algorithm (GA-INSA) and an improved LeNet-5 CNN. The GA enhances detector generation and distribution in NSA while SMOTE handles class imbalance for CNN. Experiments show GA-INSA has over 9% higher accuracy than NSA, SVM and GA-BP across different data sizes. The improved LeNet-5 demonstrates superior accuracy and recall rates over by over 20% baseline LeNet-5. However, more comprehensive evaluation on public datasets, design details on architectures, and discussion around limitations are warranted. The data shows that the designed network intrusion detection model has better performance. The model can provide technical support for network intrusion detection in reality and can enrich the content of network intrusion detection technology.

Research on Adaptive 1DCNN Network Intrusion Detection Technology Based on BSGM Mixed Sampling.

The development of internet technology has brought us benefits, but at the same time, there has been a surge in network attack incidents, posing a serious threat to network security. In the real world, the amount of attack data is much smaller than normal data, leading to a severe class imbalance problem that affects the performance of classifiers. Additionally, when using CNN for detection and classification, manual adjustment of parameters is required, making it difficult to obtain the optimal number of convolutional kernels. Therefore, we propose a hybrid sampling technique called Borderline-SMOTE and Gaussian Mixture Model (GMM), referred to as BSGM, which combines the two approaches. We utilize the Quantum Particle Swarm Optimization (QPSO) algorithm to automatically determine the optimal number of convolutional kernels for each one-dimensional convolutional layer, thereby enhancing the detection rate of minority classes. In our experiments, we conducted binary and multi-class experiments using the KDD99 dataset. We compared our proposed BSGM-QPSO-1DCNN method with ROS-CNN, SMOTE-CNN, RUS-SMOTE-CNN, RUS-SMOTE-RF, and RUS-SMOTE-MLP as benchmark models for intrusion detection. The experimental results show the following: (i) BSGM-QPSO-1DCNN achieves high accuracy rates of 99.93% and 99.94% in binary and multi-class experiments, respectively; (ii) the precision rates for the minority classes R2L and U2R are improved by 68% and 66%, respectively. Our research demonstrates that BSGM-QPSO-1DCNN is an efficient solution for addressing the imbalanced data issue in this field, and it outperforms the five intrusion detection methods used in this study.

A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT.

Network intrusion detection technology is key to cybersecurity regarding the Internet of Things (IoT). The traditional intrusion detection system targeting Binary or Multi-Classification can detect known attacks, but it is difficult to resist unknown attacks (such as zero-day attacks). Unknown attacks require security experts to confirm and retrain the model, but new models do not keep up to date. This paper proposes a Lightweight Intelligent NIDS using a One-Class Bidirectional GRU Autoencoder and Ensemble Learning. It can not only accurately identify normal and abnormal data, but also identify unknown attacks as the type most similar to known attacks. First, a One-Class Classification model based on a Bidirectional GRU Autoencoder is introduced. This model is trained with normal data, and has high prediction accuracy in the case of abnormal data and unknown attack data. Second, a multi-classification recognition method based on ensemble learning is proposed. It uses Soft Voting to evaluate the results of various base classifiers, and identify unknown attacks (novelty data) as the type most similar to known attacks, so that exception classification becomes more accurate. Experiments are conducted on WSN-DS, UNSW-NB15, and KDD CUP99 datasets, and the recognition rates of the proposed models in the three datasets are raised to 97.91%, 98.92%, and 98.23% respectively. The results verify the feasibility, efficiency, and portability of the algorithm proposed in the paper.

Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU.

To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.

Intelligent Intrusion Detection Method of Industrial Internet of Things Based on CNN-BiLSTM

Aiming at the problems of fuzzy detection characteristics, high false positive rate and low accuracy of traditional network intrusion detection technology, an improved intelligent intrusion detection method of industrial Internet of Things based on deep learning is proposed. Firstly, the data set is preprocessed and transformed into 122 dimensional intrusion data set after one-hot coding; Secondly, aiming at the problem that convolution network cannot deal with data with long-distance attributes, Bidirectional long short-term memory (BiLSTM) is used to mine the relationship between data features; At the same time, the Batch Normalization mechanism is introduced to speed up the training of deep neural network. After the activation function performs nonlinear transformation on the input data of the previous layer, it is normalized to ensure the trainability of the network. The experimental results on NSL-KDD data set show that the accuracy of the proposed CNN-BiLSTM model is 96.3%, the detection rate is 97.1%, and the performance is the best.

Research on network security technology of industrial control system

The relationship between industrial control system and Internet is becoming closer and closer, and its network security has attracted much attention. Penetration testing is an active network intrusion detection technology, which plays an indispensable role in protecting the security of the system. This paper mainly introduces the principle of penetration testing, summarizes the current cutting-edge penetration testing technology, and looks forward to its development.

Research on Network Intrusion Detection Technology Based on Machine Learning

Research on Network Intrusion Detection Technology Based on Machine Learning

Research on Intrusion Detection Technology Based on Computer Network

After entering the 21st century, with the continuous progress and development of China’s social economy, China’s science and technology has also been rapid development, computer in this context, has gradually spread to people’s daily life and work, people are increasingly inseparable from the use of computer. However, due to the existence of computer network security performance risks, and criminals want to use these loopholes for illegal activities, so computer network security has become a focus of increasing concern, related to all aspects of people’s daily life. The purpose of this paper is to discuss and study the role and significance of computer network intrusion detection technology, introduce network intrusion methods, and put forward relevant measures and methods to improve computer network security.

RETRACTED ARTICLE: Research on network intrusion detection technology based on improved FCMA algorithm

In the process of science and technology development, although the control object is becoming more and more complex, the requirements for control performance are increasing. The contradiction of traditional control theory in FCMA algorithm is increasingly prominent in practical application, especially in robot control research and learning control, which is in urgent need of new vitality. At this time, a new control mode in the field of control, namely intelligent control, arises at the historic moment, which is a leap forward of control mode. In recent decades, the rapid development of the Internet and the development of a large scale, to avoid the belt to a variety of network intrusion, these network intrusion almost every moment. Network intrusion detection is an important way of computer security detection. It can identify the flow of external intrusion in the network, and carry out early warning processing. The network intrusion detection based on FCMA transforms the intrusion detection into the classification of network traffic, establishes a network traffic classification model, and classifies the network traffic by using machine learning classification standards, and detects the external intrusion traffic. Based on FCMA algorithm, this paper explores the method of network intrusion identification, and focuses on the data imbalance processing and feature selection in the process of network intrusion identification.

Research on Network Intrusion Detection Method Based on Machine Learning

After years of development, the Internet has penetrated into all areas of society, and various network intrusions occur almost every day. In order to enhance the security of the network, intrusion detection technology has received more and more attention. Network intrusion detection is a branch of computer security. Its goal is to automatically and effectively detect intrusion traffic in the network and provide timely warnings. Network intrusion detection technology has become an important means to resist network intrusion in recent years, and network intrusion detection systems of various scales have been applied in the network. Due to the diversity of intrusion methods, traditional digital authentication and firewalls and other network security measures are difficult to meet the needs of network intrusion detection. The machine learning boom has gradually emerged, and machine learning methods have begun to be applied to the field of intrusion detection, which has become a research hotspot in this field. This article discusses machine learning methods, discusses adding machine learning algorithms to the network intrusion detection link to achieve effective detection of network intrusion traffic, so as to obtain a good network intrusion detection effect.

Research on Computer Network Technology System Based on Artificial Intelligence Technology

This article introduces computer network intrusion detection systems and their classification based on artificial intelligence technology, and points out the challenges that intrusion detection systems (IDS) are facing in wireless sensor computer networks. On this basis, a hierarchical multi-layer wireless sensor network intrusion detection system and technology based on artificial intelligence technology Agent is proposed, and its network structure, working principle and performance are analysed.

A Review of Intrusion Detection Technology Based on Deep Rein-forcement Learning

<p>With the rapid development of modern science and technology, all kinds of network attacks are updated constantly. Therefore, the traditional network security defense mechanism needs to be further improved. Through extensive investigation, this paper presents the latest work of network intrusion detection technology based on deep learning. Firstly, this paper introduces the related concepts of network intrusion detection technology. On this basis, we further evaluate the performance of three common deep learning models in intrusion detection, and conclude that DBN algorithm has some strong advantages. Afterwards, it also puts forward several improvement strategies of intrusion detection models.</p>

Research on Network Intrusion Detection Technology Based on Network Security

Research on Network Intrusion Detection Technology Based on Network Security

Research on computer network information security based on improved machine learning

The 21st century is an era of rapid development of the Internet. Internet technology is widely used in various fields. With the rapid development of network, the importance of network information security is also highlighted. The traditional network information security technology has been difficult to ensure the security of network information. Therefore, we mainly study the application of machine learning feature extraction method in situational awareness system. A feature selection method based on machine learning is proposed to extract situational features.By analyzing whether the background of network information is safe or not, and according to the current research situation at home and abroad and the trend of Internet development, this paper tries out the practical application of machine learning feature extraction method in a certain perception system. Based on the above points, a selection method based on machine learning is proposed to extract situational features. The accuracy and timeliness of situational awareness system detection are seriously affected by the high dimension, noise and redundant features of massive network traffic data.Therefore, it is of great value to further study network intrusion detection technology on the basis of machine learning.

An improved method in deep packet inspection based on regular expression

The continuous development of Internet technology makes the network intrusion detection technology get more and more attention. Deep packet inspection technology as an effective network intrusion detection technology can play a huge role in network security. Deep packet inspection technology is a kind of network intrusion detection technology applied to the application layer in detail, rather than only detecting the port information of the packet. The regular expression matching technology is a key technology in deep packet inspection because of the rich semantics and flexibility of regular expressions. However, a huge number of transfer edges exist when the matching algorithm is being applied, which will lead to an increase in memory usage of the algorithm. In this paper, we propose an improved method of concatenating transfer edges. By using character interval, several consecutive characters are represented by character intervals, which can reduce the number of transfer edges effectively. In addition, a comparison experiment is given to compare the two methods which are before and after the improvement. It shows that the number of transfer edges can be reduced to 10% of that before improvement and the efficiency of deep packet inspection is improved.

Research on Intrusion Detection Technology for Ad Hoc Network

As the second line of defense, intrusion detection is the necessary means of getting the high survivability. Intrusion detection technology and its classification is described in this article. And based on the features of Ad Hoc network, the existing new network intrusion detection technology and its characteristics suitable for Ad Hoc network are summarized.

Research on Intrusion Detection Technology Based on Ad Hoc Network

As the second line of defense, intrusion detection is the necessary means of getting the high survivability. Intrusion detection technology and its classification is described in this article. And based on the features of Ad Hoc network, the existing new network intrusion detection technology and its characteristics suitable for Ad Hoc network are summarized.

The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network

The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.

The Study on Network Intrusion Detection Technology Based on Improved Principal Component Analysis

The Study on Network Intrusion Detection Technology Based on Improved Principal Component Analysis