计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 514-517.doi: 10.11896/jsjkx.200700158

• 信息安全 • 上一篇    下一篇

基于决策树的车联网安全态势预测模型研究

唐亮, 李飞   

  1. 成都信息工程大学网络空间安全学院 成都610225
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 唐亮(tangliang_cuit@163.com)
  • 基金资助:
    四川省自然科学基金(2019YFG0201);成都市科技项目(2018-YF05-00707-SN)

Research on Forecasting Model of Internet of Vehicles Security Situation Based on Decision Tree

TANG Liang, LI Fei   

  1. School of Cybersecurity,Chengdu University of Information Technology,Chengdu 610225,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:TANG Liang,born in 1995,M.S.candidate.His main research interest includes X vehicle security.
  • Supported by:
    Natural Science Foundation of Sichuan Province(2019YFG0201) and Chengdu Science and TechnologyProject(2018-YF05-00707-SN).

摘要: 随着车辆智能技术的发展,网络与车辆的结合成为了必然,给人们带来了极大的便利。同时,黑客还可以利用技术漏洞攻击车辆,从而导致严重的交通事故。基于这种情况,车辆信息安全保护技术逐渐成为人们关注的焦点。面对层出不穷的车联网网络攻击,需要态势感知对车联网进行保驾护航,为了提高车联网安全态势感知的准确度,文中提出了基于决策树的车联网安全态势预测模型,由于网络攻击往往由某些特定的属性发生异常变化,属性变化的过程就是一种攻击方式,决策树根据这些属性分类,使用信息增益率来构建决策树,并推导出决策的规则。通过实验验证了所提算法在车联网安全态势感知中的可行性以及预测结果的准确性。

关键词: 车联网安全, 决策树, 态势感知

Abstract: With the development of vehicle intelligent technology,the combination of network and vehicle becomes inevitable,which brings great convenience to people.At the same time,hackers can also use technical loopholes to attack vehicles,resulting in serious traffic accidents and even vehicle crashes.Based on this situation,vehicle information security technology has gradually become the focus of attention.In the face of endless network attacks on Internet of vehicles,situation awareness is needed to protect the Internet of vehicles.In order to improve the accuracy of IOV security situation awareness,this paper proposes a decision tree-based IOV security situation prediction model.Because network attacks often change abnormally by certain specific attri-butes,the process of attribute change is an attack method.The tree is classified according to these attributes,the information gain rate is used to build a decision tree,and the rules for decision are derived.Through experiments,the feasibility of the proposed algorithm in the security situation awareness of the Internet of Vehicles and the accuracy of the prediction results are verified.

Key words: Decision tree, Internet of vehicles security, Situation awareness

中图分类号: 

  • TP391
[1] Annual report ofintelligent connected vehicles in 2019[OL].https://skygo.360.cn/2020/03/24/360-skygo-2019-icv-cybersecurity-annual-report/.
[2] LI X H,ZHONG C,CHEN Y,et al.Overview of Internet of vehicles security [J].Journal of Information Security,2019,4(3):17-33.
[3] CHANG Y H,MA Z R,LI X,et al.Overview of network security situation awareness [J].Cyberspace Security,2019,10 (12):88-93.
[4] ENDSLEY M R.Situation Awareness Global Assessment Technique(SAGAT)[C]//Proceedings of the IEEE 1988 National Aerospaceand Electronics Conference.Piscataway:IEEE,1988:789-795.
[5] BASS T.Intrusion detection systems &multisensor data fusion:cre-ating cyberspace situational awareness[J].Communications of the ACM,1999,43(4):99-105.
[6] LIU J W,LIU J J,LU Y L,et al.Application of game theory in network security situation awareness [J].Computer Applications,2017,37 (S2):48-51,64.
[7] DING H,XU H H,DUAN R,et al.Security situation awareness model based on Bayesian method [J/OL].Computer Engineering:1-12.[2020-04-16].https://doi.org/10.19678/j.issn.1000-3428.0055219.
[8] JIANG Y,LI C H,WEI X H,et al.Research on network security situation prediction based on Improved PSO and optimized RBF [J].Measurement and Control Technology,2018,37(5):56-60.
[9] LI X.Research on network security situation evaluation based on particle swarm optimization neural network [D].Hebei Normal University,2018
[10] XU G,CAO Y,REN Y,et al.Network Security SituationAwareness Based on Semantic Ontology and User-Defined Rules for Internet of Things[C]//IEEE Access.2017:21046-21056.
[11] HE F,ZHANG Y,LIU H.A Novel Approach for Security Situational Awareness in the Internet of Things[J].arXiv:1711.10182,2017.
[12] MCELWEE S,CANNADY J.Cyber Situation Awareness withActive Learning for Intrusion Detection[J].arXiv:1912.12673,2019.
[13] PENG K,LEUNG V C M,ZHENG L X,et al.Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment[OL].https://doi.org/10.1155/2018/4680867.
[14] Machine learning [M].Tsinghua University Press,2016:425.
[15] https://sites.google.com/a/hksecurity.net/ocslab/Datasets/da-tachallenge2019/car.
[1] 吕鹏鹏, 王少影, 周文芳, 连阳阳, 高丽芳.
基于进化神经网络的电力信息网安全态势量化方法
Quantitative Method of Power Information Network Security Situation Based on Evolutionary Neural Network
计算机科学, 2022, 49(6A): 588-593. https://doi.org/10.11896/jsjkx.210200151
[2] 宋涛, 李秀华, 李辉, 文俊浩, 熊庆宇, 陈杰.
大数据时代下车联网安全加密认证技术研究综述
Overview of Research on Security Encryption Authentication Technology of IoV in Big Data Era
计算机科学, 2022, 49(4): 340-353. https://doi.org/10.11896/jsjkx.210400112
[3] 任首朋, 李劲, 王静茹, 岳昆.
基于集成回归决策树的lncRNA-疾病关联预测方法
Ensemble Regression Decision Trees-based lncRNA-disease Association Prediction
计算机科学, 2022, 49(2): 265-271. https://doi.org/10.11896/jsjkx.201100132
[4] 刘振宇, 宋晓莹.
一种可用于分类型属性数据的多变量回归森林
Multivariate Regression Forest for Categorical Attribute Data
计算机科学, 2022, 49(1): 108-114. https://doi.org/10.11896/jsjkx.201200189
[5] 曹扬晨, 朱国胜, 祁小云, 邹洁.
基于随机森林的入侵检测分类研究
Research on Intrusion Detection Classification Based on Random Forest
计算机科学, 2021, 48(6A): 459-463. https://doi.org/10.11896/jsjkx.200600161
[6] 丁思凡, 王锋, 魏巍.
一种基于标签相关度的Relief特征选择算法
Relief Feature Selection Algorithm Based on Label Correlation
计算机科学, 2021, 48(4): 91-96. https://doi.org/10.11896/jsjkx.200800025
[7] 赵冬梅, 宋会倩, 张红斌.
基于时间因子和复合CNN结构的网络安全态势评估
Network Security Situation Based on Time Factor and Composite CNN Structure
计算机科学, 2021, 48(12): 349-356. https://doi.org/10.11896/jsjkx.210400227
[8] 董明刚, 黄宇扬, 敬超.
基于遗传实例和特征选择的K近邻训练集优化方法
K-Nearest Neighbor Classification Training Set Optimization Method Based on Genetic Instance and Feature Selection
计算机科学, 2020, 47(8): 178-184. https://doi.org/10.11896/jsjkx.190700089
[9] 李欣, 段詠程.
基于改进隐马尔可夫模型的网络安全态势评估方法
Network Security Situation Assessment Method Based on Improved Hidden Markov Model
计算机科学, 2020, 47(7): 287-291. https://doi.org/10.11896/jsjkx.190300045
[10] 朱涤尘, 夏换, 杨秀璋, 于小民, 张亚成, 武帅.
基于文本挖掘和决策树分析的中国手游产业发展研究
Research on Mobile Game Industry Development in China Based on Text Mining and Decision Tree Analysis
计算机科学, 2020, 47(6A): 530-534. https://doi.org/10.11896/JsJkx.190700124
[11] 白雪, 努尔布力, 王亚东.
网络安全态势感知研究现状与发展趋势的图谱分析
Map Analysis for Research Status and Development Trend on Network Security Situational Awareness
计算机科学, 2020, 47(6A): 340-343. https://doi.org/10.11896/JsJkx.190500169
[12] 邹洁, 朱国胜, 祁小云, 曹扬晨.
基于C4.5决策树的HTTPS加密流量分类方法
HTTPS Encrypted Traffic Classification Method Based on C4.5 Decision Tree
计算机科学, 2020, 47(6A): 381-385. https://doi.org/10.11896/JsJkx.191200155
[13] 王海涛, 宋丽华, 向婷婷, 刘力军.
人工智能发展的新方向——人机物三元融合智能
New Development Direction of Artificial Intelligence-Human Cyber Physical Ternary Fusion Intelligence
计算机科学, 2020, 47(11A): 1-5. https://doi.org/10.11896/jsjkx.200100053
[14] 董本清, 李凤坤.
基于加权划分非平衡决策树的诗歌朗读情感度分析
Analysis of Emotional Degree of Poetry Reading Based on WDOUDT
计算机科学, 2020, 47(11A): 46-51. https://doi.org/10.11896/jsjkx.200600055
[15] 王婷婷, 朱江.
基于差分WGAN的网络安全态势预测
Network Security Situation Forecast Based on Differential WGAN
计算机科学, 2019, 46(11A): 433-437.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!