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Chiba, Zouhair, Abghour, Noreddine, Moussaid, Khalid, Omri, Amina El, Rida, Mohamed.  2018.  A Hybrid Optimization Framework Based on Genetic Algorithm and Simulated Annealing Algorithm to Enhance Performance of Anomaly Network Intrusion Detection System Based on BP Neural Network. 2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). :1—6.

Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Peng, Wang, Kong, Xiangwei, Peng, Guojin, Li, Xiaoya, Wang, Zhongjie.  2019.  Network Intrusion Detection Based on Deep Learning. 2019 International Conference on Communications, Information System and Computer Engineering (CISCE). :431–435.
With the continuous development of computer network technology, security problems in the network are emerging one after another, and it is becoming more and more difficult to ignore. For the current network administrators, how to successfully prevent malicious network hackers from invading, so that network systems and computers are at Safe and normal operation is an urgent task. This paper proposes a network intrusion detection method based on deep learning. This method uses deep confidence neural network to extract features of network monitoring data, and uses BP neural network as top level classifier to classify intrusion types. The method was validated using the KDD CUP'99 dataset from the Lincoln Laboratory of the Massachusetts Institute of Technology. The results show that the proposed method has a significant improvement over the traditional machine learning accuracy.
Zhang, Xu, Ye, Zhiwei, Yan, Lingyu, Wang, Chunzhi, Wang, Ruoxi.  2018.  Security Situation Prediction based on Hybrid Rice Optimization Algorithm and Back Propagation Neural Network. 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). :73—77.
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.
Fu, Tian, Lu, Yiqin, Zhen, Wang.  2019.  APT Attack Situation Assessment Model Based on optimized BP Neural Network. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). :2108—2111.
In this paper, it first analyzed the characteristics of Advanced Persistent Threat (APT). according to APT attack, this paper established an BP neural network optimized by improved adaptive genetic algorithm to predict the security risk of nodes in the network. and calculated the path of APT attacks with the maximum possible attack. Finally, experiments verify the effectiveness and correctness of the algorithm by simulating attacks. Experiments show that this model can effectively evaluate the security situation in the network, For the defenders to adopt effective measures defend against APT attacks, thus improving the security of the network.
Wang, Dongqi, Shuai, Xuanyue, Hu, Xueqiong, Zhu, Li.  2019.  Research on Computer Network Security Evaluation Method Based on Levenberg-Marquardt Algorithms. 2019 International Conference on Communications, Information System and Computer Engineering (CISCE). :399—402.
As we all know, computer network security evaluation is an important link in the field of network security. Traditional computer network security evaluation methods use BP neural network combined with network security standards to train and simulate. However, because BP neural network is easy to fall into local minimum point in the training process, the evalu-ation results are often inaccurate. In this paper, the LM (Levenberg-Marquard) algorithm is used to optimize the BP neural network. The LM-BP algorithm is constructed and applied to the computer network security evaluation. The results show that compared with the traditional evaluation algorithm, the optimized neural network has the advantages of fast running speed and accurate evaluation results.
Guan, Chengli, Yang, Yue.  2019.  Research of Computer Network Security Evaluation Based on Backpropagation Neural Network. 2019 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :181—184.
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.
Xuefeng, He, Chi, Zhang, Yuewu, Jing, Xingzheng, Ai.  2019.  Risk Evaluation of Agricultural Product Supply Chain Based on BP Neural Network. 2019 16th International Conference on Service Systems and Service Management (ICSSSM). :1–8.

The potential risk of agricultural product supply chain is huge because of the complex attributes specific to it. Actually the safety incidents of edible agricultural product emerge frequently in recent years, which expose the fragility of the agricultural product supply chain. In this paper the possible risk factors in agricultural product supply chain is analyzed in detail, the agricultural product supply chain risk evaluation index system and evaluation model are established, and an empirical analysis is made using BP neural network method. The results show that the risk ranking of the simulated evaluation is consistent with the target value ranking, and the risk assessment model has a good generalization and extension ability, and the model has a good reference value for preventing agricultural product supply chain risk.

Yi, Weiming, Dong, Peiwu, Wang, Jing.  2018.  Node Risk Propagation Capability Modeling of Supply Chain Network Based on Structural Attributes. Proceedings of the 2018 9th International Conference on E-business, Management and Economics. :50–54.
This paper firstly defines the importance index of several types of nodes from the local and global attributes of the supply chain network, analyzes the propagation effect of the nodes after the risk is generated from the perspective of the network topology, and forms multidimensional structural attributes that describe node risk propagation capabilities of the supply chain network. Then the indicators of the structure attributes of the supply chain network are simplified based on PCA (Principal Component Analysis). Finally, a risk assessment model of node risk propagation is constructed using BP neural network. This paper also takes 4G smart phone industry chain data as an example to verify the validity of the proposed model.
Zhang, Q., Ma, Z., Li, G., Qian, Z., Guo, X..  2016.  Temperature-dependent demagnetization nonlinear Wiener model with neural network for PM synchronous machines in electric vehicle. 2016 19th International Conference on Electrical Machines and Systems (ICEMS). :1–4.

The inevitable temperature raise leads to the demagnetization of permanent magnet synchronous motor (PMSM), that is undesirable in the application of electrical vehicle. This paper presents a nonlinear demagnetization model taking into account temperature with the Wiener structure and neural network characteristics. The remanence and intrinsic coercivity are chosen as intermediate variables, thus the relationship between motor temperature and maximal permanent magnet flux is described by the proposed neural Wiener model. Simulation and experimental results demonstrate the precision of temperature dependent demagnetization model. This work makes the basis of temperature compensation for the output torque from PMSM.