Network Security Situation Prediction Based on MR-SVM

Zhang, Qikun; Ma, Dongyan; Liu, Chen; Shi, Zhiyu; Yan, Hao; Hu, Changzhen

doi:10.1109/access.2019.2939490

Cited by 44 publications

(30 citation statements)

References 42 publications

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“…Network security situation prediction means predicting the future network situation according to the current network state and historical data. In recent years, the rapid development of machine learning provides a new solution for network situation prediction, such as the support vector machine (SVM) [ 12 ] and hidden Markov model (HMM) [ 13 ]. Then, deep learning applies to network security situation assessment: for example, [ 14 ] summarizes the artificial intelligence related to network security as well as the progress and challenges of current research; [ 15 ] studies the performance of different neural networks in the NSSP; and [ 16 ] proposes an LSTM network security situation prediction model based on the sigmoid weighted reinforcement mechanism, which can improve the convergence rate.…”

Section: Related Workmentioning

confidence: 99%

Research on Network Security Situation Awareness Based on the LSTM-DT Model

Zhang

Kang

Xiao

2021

Sensors

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To better understand the behavior of attackers and describe the network state, we construct an LSTM-DT model for network security situation awareness, which provides risk assessment indicators and quantitative methods. This paper introduces the concept of attack probability, making prediction results more consistent with the actual network situation. The model is focused on the problem of the time sequence of network security situation assessment by using the decision tree algorithm (DT) and long short-term memory(LSTM) network. The biggest innovation of this paper is to change the description of the network situation in the original dataset. The original label only has attack and normal. We put forward a new idea which regards attack as a possibility, obtaining the probability of each attack, and describing the network situation by combining the occurrence probability and attack impact. Firstly, we determine the network risk assessment indicators through the dataset feature distribution, and we give the network risk assessment index a corresponding weight based on the analytic hierarchy process (AHP). Then, the stack sparse auto-encoder (SSAE) is used to learn the characteristics of the original dataset. The attack probability can be predicted by the processed dataset by using the LSTM network. At the same time, the DT algorithm is applied to identify attack types. Finally, we draw the corresponding curve according to the network security situation value at each time. Experiments show that the accuracy of the network situation awareness method proposed in this paper can reach 95%, and the accuracy of attack recognition can reach 87%. Compared with the former research results, the effect is better in describing complex network environment problems.

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Section: Related Workmentioning

confidence: 99%

Research on Network Security Situation Awareness Based on the LSTM-DT Model

Zhang

Kang

Xiao

2021

Sensors

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“…The authors used seeker optimization algorithm to find the best weight and introduced simulated annealing algorithm to improve the global search ability of the algorithm, but BP neural network is not suitable for time series data. Hu et al [6] proposed a situation prediction algorithm of SVM based on MapReduce to solve the problem that the training time of SVM is long when there are a large number of samples. Compared with the traditional model, this prediction model improves the prediction accuracy and reduces the training time.…”

Section: Related Workmentioning

confidence: 99%

Network security situation prediction based on feature separation and dual attention mechanism

Zhao

et al. 2021

J Wireless Com Network

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With the development of smart cities, network security has become more and more important. In order to improve the safety of smart cities, a situation prediction method based on feature separation and dual attention mechanism is presented in this paper. Firstly, according to the fact that the intrusion activity is a time series event, recurrent neural network (RNN) or RNN variant is used to stack the model. Then, we propose a feature separation method, which can alleviate the overfitting problem and reduce cost of model training by keeping the dimension unchanged. Finally, limited attention is proposed according to global attention. We sum the outputs of the two attention modules to form a dual attention mechanism, which can improve feature representation. Experiments have proved that compared with other existing prediction algorithms, the method has higher accuracy in network security situation prediction. In other words, the technology can help smart cities predict network attacks more accurately.

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“…The results showed that the method had more accurate prediction results, although the training time was long. Aiming at the problem of long training time of the support vector machine (SVM) algorithm, Hu et al [11] optimized the SVM algorithm with the cuckoo search (CS), carried out distributed training on MapReduce, and found that the method could effectively reduce the training time. Zhou et al [12] solved the NSSP problem with the hidden belief rule base (HBRB), improved the prediction accuracy of the model based on the evidence reasoning rules, and verified the effectiveness of the new method by the case study.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Network Security Situation Awareness based on an Improved Model Combined with Neural Network

2021

Preprint

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People always pay attention to the security of the network. This paper mainly analyzed the problem of network security situation prediction (NSSP). The Radial Basis Function (RBF) neural network was improved by the particle swarm optimization (PSO) algorithm, and a modified PSO (MPSO)-RBF algorithm was obtained, which was used as the prediction model. Then, the data from National Internet Emergency Center (CNCERT/CC) were used as the experimental data, and the MPSO-RBF algorithm was compared with RBF and PSO-RBF algorithms. The results showed that the MPSO-RBF algorithm could achieve convergence in about 50 times of iterations, showing a high calculation efficiency, and the mean absolute percentage error (MAPE) value, mean square error (MSE) value, and root-mean-square error (RMSE) value were small, 2.13%, 0.0005, and 0.0224, respectively, showing that the algorithm had good prediction performance. The results verify the reliability of the MPSO-RBF algorithm in NSSP, which is conducive to further improve network security.

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Network Security Situation Prediction Based on MR-SVM

Cited by 44 publications

References 42 publications

Research on Network Security Situation Awareness Based on the LSTM-DT Model

Research on Network Security Situation Awareness Based on the LSTM-DT Model

Network security situation prediction based on feature separation and dual attention mechanism

Prediction of Network Security Situation Awareness based on an Improved Model Combined with Neural Network

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