Application of Improved BP Neural Network with Correlation Rules in Network Intrusion Detection

Yongfeng Cui,Xiangqian Li Ma,Zhijie Liu 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.4

To detect various network attacks in real time, this paper developed a network intrusion detection system based on artificial neural network. This paper first introduced the recent development of neural network, BP algorithm and structure of a simple perceptron. Then, this paper developed an improved BP neural network algorithm to detect anomaly network traffic with adjusted correlation rules. Finally, the network intrusion system in this paper was tested in a real network situation; the improved BP algorithm neural network with adjusted correlation rules shows a reduction in total error and increment in alarm rate compared to the traditional basic BP algorithm model.

Application of BP Neural Network Model based on Particle Swarm Optimization in Enterprise Network Information Security

Shumei liu 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.3

The development of network technology has brought convenience to people's life, but also provides the convenience for the virus, Trojan and other destructive programs to attack the network. Then, the computer network security is becoming more and more dangerous. Accurately and scientifically predict the risk of network, it can effectively prevent the risk, and reduce the loss caused by the problem of computer network security. Computer network security is an early warning problem of multi index system. So, the traditional linear forecasting method cannot accurately describe the impact of each index on the evaluation results, and the accuracy of the prediction results is low. In order to improve the prediction accuracy of computer network security, this paper presents a new forecasting method for computer network security. Firstly, the evaluation index of computer network security is selected by expert system, and the weight of evaluation index is determined by the expert scoring method. Secondly, we put the index weight into the BP neural network, and use the BP neural network to learn it. Then, the parameters of BP neural network are optimized by the improved particle swarm optimization algorithm. After that, this paper uses a method based on the Fibonacci method principle to find the number of hidden layer node which has the best fitting ability. Finally, we use this algorithm to predict the network security of a certain enterprise in the next six months. The score is 0.67, 0.84, 0.72, 0.87, 0.86 and 0.91, which is close to the actual value of network security.

The Early Warning Research of Enterprise Financial Crisis Based on BP Neural Network

Yang Xiaobin 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.9

Compared with previous studies on artificial neural network, this paper expounds the artificial neural network theory, and gives information transfer function and mathematical models of BP neural network. This paper has selected 100 financial crisis companies and 100 non-financial companies as samples crisis, which are in line with the definition of the financial crisis. They has established the enterprise financial crisis model by using BP neural network algorithm, also made a sample test, and the accuracy rate is up to 80%.

최적화된 신경망 기반 무선 센서 노드위치 알고리즘 제안

관보(Bo Guan),쥐훙샹(Hongxiang Qu),양펑지옌(Fengjian Yang),리홍량(Hongliang Li),정양권(Yang-Kwon Jeong) 한국전자통신학회 2022 한국전자통신학회 논문지 Vol.17 No.6

본 연구는 RSSI의 거리측정 방법이 외부 환경에 의해 쉽게 영향을 받아 위치 오차가 크다는 결점을 도출하였고 이 3차원 배치 환경에서 RSSI의 거리측정 노드에서 측정한 거리값을 최적화하는 문제에 대해 향상된 CA-PSO 알고리즘을 개선한 CA-PSO-BP 알고리즘을 제안하였다. 제안된 알고리즘은 3차원 무선센서네트워크(WSN) 공간에서 인식할 수 없는 노드를 설정할 수 있도록 하였다. 또한, CA-PSO를 BP 신경망에 응용하므로, 학습을 통해 BP 네트워크의 학습시간 단축과 알고리즘의 수렴 속도를 제고 할 수 있었다. 본 연구에서 제안한 알고리즘을 통해 네트워크의 위치의 정밀도를 현저(15%)하게 높일 수 있다는 것을 증명하였고 유의미한 결과를 얻을 수 있었다. This study leads to the shortcoming that the RSSI distance measurement method is easily affected by the external environment and the position error is large, leading to the problem of optimizing the distance values measured by the RSSI distance measurement nodes in this three-dimensional configuration environment. We proposed the CA-PSO-BP algorithm, which is an improved version of the CA-PSO algorithm. The proposed algorithm allows setting unknown nodes in WSN 3D space. In addition, since CA-PSO was applied to the BP neural network, it was possible to shorten the learning time of the BP network and improve the convergence speed of the algorithm through learning.Through the algorithm proposed in this study, it was proved that the precision of the network location can be increased significantly (15%), and significant results were obtained.

Application of Data Fusion Technology Based on Weight Improved Particle Swarm Optimization Neural Network Algorithm in Wireless Sensor Networks

Xiajun Ding,Hongbo Bi,Xiaodan Jiang,Lu zhang 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.3

With the development of sensor technology, network technology, embedded control technology and wireless communication technology, the application of wireless sensor networks (WSN) has become more and more widely. Wireless sensor networks have been named the most influential and important technology of the world in twenty-first Century. In wireless sensor networks, data fusion is an important research branch. In this paper, a data prediction model of wireless sensor network based on weight improved particle swarm optimization neural network algorithm is proposed. In view of the deficiency of the traditional BP neural network model, this paper combines with the characteristics of the data prediction model, and the BP neural network model is improved and integrated. After that, we train the neural network's sample set, and add the momentum item to correct the weight, so that the neural network can be predicted more quickly and accurately. The main idea of this paper is to predict the future data based on the historical data which are collected by sensor nodes, so as to achieve the purpose of reducing the amount of data transmission in the network and saving the energy of nodes. Finally, the experimental results show that the improved particle swarm optimization algorithm based on weight improved particle swarm optimization neural network algorithm has higher accuracy than the multiple regression method and the grey prediction method. In addition, the method can be used to effectively save energy in wireless sensor data transmission.

An Application of BP Neural Network to the Prediction of Compressive Strength in Circular Concrete Columns Confined with CFRP

Khalil AL-Bukhaiti,Liu Yanhui,Zhao Shichun,Hussein Abas 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.7

The neural network comprises many neurons with extensive interconnections operatingparallel and performing specific functions. This paper establishes a BP neural networkprediction model for the compressive strength of CFRP-confined concrete based on a largenumber of experimental data to study the predictive ability of the BP neural network on thecompressive strength of CFRP-confined concrete and the output performance of the neuralnetwork model. The model is based on a BP neural network that has been trained using manyexperimental data. An investigation is being conducted on the effect of different datacombinations on the accuracy of the predictions made by the neural network model. Thehigh-precision BP network model is created into generic and simplified formulae for applicationconvenience. These formulas are developed based on the theory of neural networks. Theneural network models' findings and the empirical formulae for making predictions arecompared and discussed. The BP neural network accurately predicts the compressive strengthof CFRP-confined concrete, with over 90% of its data points having less than 15% error. Incomparison, the regression model shows less accuracy, with less than 70% of its data pointshaving an error within 15%. Compared to traditional regression models, the simple linearequation derived using Purelin instead of Sigmoid as the transfer function only adds a constantterm. The average value of prediction/test results is 1.011. The analysis results show that BPneural network can extract the input and output parameters' data information well and obtaina high-accuracy prediction model. The coefficient of variation is 0.112, which indicates thatthe prediction accuracy and stability are greater than average.

A Data Fusion Algorithm Based on Neural Network Research in Building Environment of Wireless Sensor Network

Tian Le,Zhao Jing 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.4

Data fusion in wireless sensor network is a effective method to reduce the network energy consumption. In order to build high performance of data fusion system, a data fusion algorithm using BP neural network to optimize fuzzy prediction and train the membership degree of collecting data is presented, which is used to determine which kind of dividing fusion mechanism is belonged for the sensor’s data collected at a given moment. First the fuzzy prediction is used for acquisition of knowledge which data is simplified to remove redundant properties and samples. The BP neural network is used to process fuzzy prediction and finally the patterns of muli-sensed-data(temperature as an example) fusion distribution are formed. Two kinds of different BP neural network are proposed and compared for more precision of the fuzzy prediction result. Second data fusion based on the fuzzy prediction will be implemented to reduce the number of data transmission in the network. Simulation results show that the algorithm has good precision and applicability.

Intrusion Detection Method based on Improved BP Neural Network Research

Zhu YuanZhong 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.5

With the development of computer network technology, more closely the relationship between people and network.The current network security problem has also been gradually into the public's field of vision, actively carried out on the network intrusion detection becomes an important direction of the development of the network security technology.On the basis of the original BP neural network, this paper puts forward an improved algorithm, and applied to network intrusion detection. After the test, the method is better than traditional convergence, better performance.

Facial Image Recognition Algorithm Based on BP Neural Network

Peihua Su 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.4

The efficiency, quality and accuracy of facial image recognition are restricted by luminance, posture, image quality, massive data and method of image recognition, etc. In response to this, this thesis proposes a facial image recognition algorithm based on BP neural network. It improves on traditional BP neutral network by constructing neutrons of facial image recognition in the input layer, hidden layer and output layer. And by constructing the network framework structure of facial image recognition, it also constructs design elements of facial image recognition from input code and output code and therefore constructs the facial image recognition algorithm based on BP neural network. This thesis verifies the algorithm through practical cases and proves that the algorithm is effective and operable.

Improved BP-NN Controller of PMSM for Speed Regulation

Li-Jia Feng,Gyu-Bum Joung 한국인터넷방송통신학회 2021 Journal of Advanced Smart Convergence Vol.10 No.2

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm for the controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.