Biblio

Recently, modern networks have been made up of connections of small devices that have less memory, small CPU capability, and limited resources. Such networks apparently known as Internet of Things networks. Devices in such network promising high standards of live for human, however, they increase the size of threats lead to bring more risks to network security. One of the most popular threats against such networks is known as Distributed Denial of Service (DDoS). Reports from security solution providers show that number of such attacks are in increase considerably. Therefore, more researches on detecting the DDoS attacks are necessary. Such works need monitoring network packets that move over Internet and networks and, through some intelligent techniques, monitored packets could be classified as benign or as DDoS attack. This work focuses on combining Neighborhood Component Analysis and Artificial Neural Network-Backpropagation to classify and identify packets as forward by attackers or as come from authorized and illegible users. This work utilized the activities of four type of the network protocols to distinguish five types of attacks from benign packets. The proposed model shows the ability of classifying packets to normal or to attack classes with an accuracy of 99.4%.

Research on network security situation awareness is currently a research hotspot in the field of network security. It is one of the easiest and most effective methods to use the BP neural network for security situation prediction. However, there are still some problems in BP neural network, such as slow convergence rate, easy to fall into local extremum, etc. On the other hand, some common used evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), easily fall into local optimum. Hybrid rice optimization algorithm is a newly proposed algorithm with strong search ability, so the method of this paper is proposed. This article describes in detail the use of BP network security posture prediction method. In the proposed method, HRO is used to train the connection weights of the BP network. Through the advantages of HRO global search and fast convergence, the future security situation of the network is predicted, and the accuracy of the situation prediction is effectively improved.

In recent years, due to the invasion of virus and loopholes, computer networks in colleges and universities have caused great adverse effects on schools, teachers and students. In order to improve the accuracy of computer network security evaluation, Back Propagation (BP) neural network was trained and built. The evaluation index and target expectations have been determined based on the expert system, with 15 secondary evaluation index values taken as input layer parameters, and the computer network security evaluation level values taken as output layer parameter. All data were divided into learning sample sets and forecasting sample sets. The results showed that the designed BP neural network exhibited a fast convergence speed and the system error was 0.000999654. Furthermore, the predictive values of the network were in good agreement with the experimental results, and the correlation coefficient was 0.98723. These results indicated that the network had an excellent training accuracy and generalization ability, which effectively reflected the performance of the system for the computer network security evaluation.