Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 348-352.

• Information Security • Previous Articles     Next Articles

Analysis Research of Software Requirement Safety Based on Neural Network and NLP

SUN Bao-hua1,3, HU Nan3, LI Dong-yang2,3   

  1. Jilin University,Changchun 130012,China1;
    Northeastern University,Shenyang 110819,China2;
    State Grid Liaoning Electric Power Co.,Ltd.,Shenyang 110004 ,China3
  • Online:2019-06-14 Published:2019-07-02

Abstract: To identify the incompleteness and ambiguity of software requirements and build a bridge between software requirements and standard specifications,this paper proposed a model of analysis and evaluation based on the Natural Language Processing (NLP) and neural network.Firstly,from ISO,the open-source Web application security plan (OWASP) and the PCI directory,multiple security specification features are identified,and text implication relationships are found.Then,the implication results and text annotations are used to train the neural network model to predict whether a certain statement in the document is available.The proposed model evaluates the performance of each implication configuration.The results show that the average F- score of the implicative configuration 9 is the highest,which is the best completeness predictor.Moreover,the performance of the proposed model is better than that of the null model under optimal and worst allocation.

Key words: Software requirements, Natural language processing, Neural networks model, Security, Null model, Implication relationships

CLC Number: 

  • TP391
[1] 陈志慧.基于Event-B的软件需求形式化建模技术的研究[D].成都:电子科技大学,2013.
[2] MALHOTRA R,CHUG A,HAYRAPETIAN A,et al.Analyzing and evaluating security features in software requirements[C]∥International Conference on Innovation and Challenges in Cyber Security.2016:26-30.
[3] 熊伟,王娟丽,蔡铭.基于QFD技术的软件可信性评估研究[J].计算机应用研究,2010,27(8):2991-2994.
[4] 王飞,郭渊博,李波,等.安全苛求软件需求规格中的安全特性验证方法[J].计算机应用,2013,33(7):2041-2045.
[5] KNAUSS E,OTT D.(Semi-) automatic Categorization of Natural Language Requirements[C]∥International Working Conference on Requirements Engineering:Foundation for Software Quality.Springer International Publishing,2014:39-54.
[6] 白川,张璇,王旭,等.可信软件非功能需求可满足性经济学方法分析[J].计算机工程与应用,2017,53(22):249-257.
[7] 张璇,李彤,王旭,等.可信软件非功能需求形式化表示与可满足分析[J].软件学报,2015,26(10):2545-2566.
[8] TAKAHASHI T,KANNISTO J,HARJU J,et al.Expressing Security Requirements:Usability of Taxonomy-Based Requirement Identification Scheme[C]∥IEEE World Congress on Services.IEEE Computer Society,2014:121-128.
[9] 徐戈,王厚峰.自然语言处理中主题模型的发展[J].计算机学报,2011,34(8):1423-1436.
[10] RANTOS K,MARKANTONAKIS K.Analysis of Potential Vulnerabilities in Payment Terminals[M]∥Secure Smart Embedded Devices,Platforms and Applications.Springer New York,2014:311-333.
[11] 倪盛俭.汉语文本蕴涵识别研究[D].武汉:武汉大学,2013.
[12] 李睿,曾俊瑀,周四望.基于局部标签树匹配的改进网页聚类算法[J].计算机应用,2010,30(3):818-820.
[13] 周冬梅.基于演化算法的智能学习与优化方法的研究[D].无锡:江南大学,2015.
[14] 伦向敏,侯一民.运用迭代最大熵算法选取最佳图像分割阈值[J].计算机工程与设计,2015,40(5):1265-1268.
[15] GOLIA S,SIMONETTO A.Treating ordinal data:a comparison between rating scale and structural equation models[J].Quality &Quantity,2015,49(3):903-915.
[1] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[2] LU Long-long, CHEN Tong, PAN Min-xue, ZHANG Tian. CodeSearcher:Code Query Using Functional Descriptions in Natural Languages [J]. Computer Science, 2020, 47(9): 1-9.
[3] TIAN Ye, SHOU Li-dan, CHEN Ke, LUO Xin-yuan, CHEN Gang. Natural Language Interface for Databases with Content-based Table Column Embeddings [J]. Computer Science, 2020, 47(9): 60-66.
[4] FENG An-ran, WANG Xu-ren, WANG Qiu-yun, XIONG Meng-bo. Database Anomaly Access Detection Based on Principal Component Analysis and Random Tree [J]. Computer Science, 2020, 47(9): 94-98.
[5] PU Hong-quan, CUI Zhe, LIU Ting,RAO Jin-tao. Comprehensive Review of Secure Electronic Voting Schemes [J]. Computer Science, 2020, 47(9): 275-282.
[6] NI Liang, WANG Nian-ping, GU Wei-li, ZHANG Qian, LIU Ji-zhao, SHAN Fang-fang. Research on Lattice-based Quantum-resistant Authenticated Key Agreement Protocols:A Survey [J]. Computer Science, 2020, 47(9): 293-303.
[7] CHEN Li-feng, ZHU Lu-ping. Encrypted Dynamic Configuration Method of FPGA Based on Cloud [J]. Computer Science, 2020, 47(7): 278-281.
[8] HUANG Yi, SHEN Guo-wei, ZHAO Wen-bo, GUO Chun. Network Representation Learning Algorithm Based on Vulnerability Threat Schema [J]. Computer Science, 2020, 47(7): 292-298.
[9] ZHANG Ying, ZHANG Yi-fei, WANG Zhong-qing and WANG Hong-ling. Automatic Summarization Method Based on Primary and Secondary Relation Feature [J]. Computer Science, 2020, 47(6A): 6-11.
[10] BAI Xue, Nurbol and WANG Ya-dong. Map Analysis for Research Status and Development Trend on Network Security Situational Awareness [J]. Computer Science, 2020, 47(6A): 340-343.
[11] GU Rong-Jie, WU Zhi-ping and SHI Huan. New Approach for Graded and Classified Cloud Data Access Control for Public Security Based on TFR Model [J]. Computer Science, 2020, 47(6A): 400-403.
[12] YAN Zhen, TIAN Yi, DUAN Zhi-guo, YU Zhen-Jiang, WANG Yu and ZHA Fan. Optimization and Design of PTN Network Security Structure [J]. Computer Science, 2020, 47(6A): 409-412.
[13] ZHANG Hao-yang and ZHOU Liang. Application of Improved GHSOM Algorithm in Civil Aviation Regulation Knowledge Map Construction [J]. Computer Science, 2020, 47(6A): 429-435.
[14] WU Xiao-kun, ZHAO Tian-fang. Application of Natural Language Processing in Social Communication:A Review and Future Perspectives [J]. Computer Science, 2020, 47(6): 184-193.
[15] LIANG Jun-bin, ZHANG Min, JIANG Chan. Research Progress of Social Sensor Cloud Security [J]. Computer Science, 2020, 47(6): 276-283.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .