计算机科学 ›› 2018, Vol. 45 ›› Issue (11): 164-168.doi: 10.11896/j.issn.1002-137X.2018.11.025
邢瑞康, 李成海, 范晓诗
XING Rui-kang, LI Cheng-hai, FAN Xiao-shi
摘要: 网电空间是在信息化发展条件下随着世界军事的重大变革而产生的新兴作战空间,尤其是在防空反导对抗方面具有十分重要的影响。由于安全机制不尽完善,网络空间所要面对的威胁也不断增多。基于此背景,文中提出一种基于IFTS预测模型的入侵检测方法,该方法通过计算网络数据各特征属性的直觉模糊来预测误差,并通过直觉模糊预测误差来区分正常数据和入侵攻击,从而达到检测预警的目的。在此基础上,建立了入侵检测框架,并通过搭建仿真实验模拟平台来模拟一个抽象的、简化的网电空间对抗模型,对算法的有效性及效能进行验证。实验结果表明,该方法是一种有效的方法,并且在一定程度上提高了模型的检测率。
中图分类号:
[1]李为民,黄仁全,王春阳,等.防空体系反制网电攻击概论.北京:解放军出版社,2013. [2]PEDRO M P,PEDRO C,HUMBERTO B,et al.Image segmentation using Atanassov’s intuitionistic fuzzy sets .Expert Systems with Applications,2013,4(1):15-26. [3]CHANDOLA V,BANERJEE A,KUMAR V.Anomaly Detection:A Survey.ACM Computing Surveys,2009,41(3):1-58. [4]CHEN Y H,MA X L,WU X Y.DDoS Detection Algorithm Based on Preprocessing Network Traffic Predicted Method and Chaos Theory.IEEE Communications letters,2013,17(5):1052-1054. [5]TAN Z Y,JAMDAGNI A,HE X J,et al.A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis .IEEE Transactions on Parallel and Distributed Systems,2013,25(2):447-456. [6]LI H Z,GUO S,LI C J,et al.A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm .Knowledge Based Systems,2013,37(2):378-387. [7]THANASIS V,ALEXANDROS P,CHRISTOS I,et al.Real-time Network Data Analysis Using Time Series Models.Simulation Modelling Practice and Theory,2012,29(29):173-180. [8]MENG F Y,CHEN X H.Entropy and similarity measure of Atanassov’s intuitionistic fuzzy sets and their application to pattern recognition based on fuzzy measures .Pattern Analysis &Applications,2016,19(1):11-20. [9]LIPPMANN R P,INGOLS K W,SCOTT C,et al.Evaluating and Strengthening Enterprise Network Security Using Attack Graphs:ESC-TR-2005-064.MIT Lincoln Laboratory,2005. [10]HUANG X W,ZHANG C.Techniques for intrusion detection based on adaptive intuitionistic fuzzy reasoning.Journal of Computer Applications,2010,30(5):1198-1201.(in Chinese) 黄孝文,张弛.基于自适应直觉模糊推理的入侵检测方法[J].计算机应用,2010,30(5):1198-1201. [11]AHMAD I,ABDULLAH A,ALGHAMDI A,et al.Optimized Intrusion Detection Mechanism using Soft Computing Techniques.Telecommunication Systems,2013,52(4):2187-2195. [12]LENG G,MCG I,PRASAD G.Design for self organizing fuzzy neural networks based on genetic algorithms .IEEE Transa-ctions on Fuzzy Systems,2006,14(6):755-766. [13]TARTAKOVSKY A G,POLUNCHENKO A S,SOKOLOV G.Efficient Computer Network Anomaly Detection by Changepoint Detection Methods .IEEE Journal of Selected Topics in Signal Processing,2013,7(1):4-11. [14]YANG Y H,HUANG H Z,SHEN Q N,et al.Reserch on intrusion detection based on Incremental GHSOM.Chinese Journal of Computers,2014,37(5):1217-1224.(in Chinese) 杨雅辉,黄海珍,沈晴霓,等.基于增量式GHSOM神经网络模型的入侵检测研究.计算机学报,2014,37(5):1217-1224. [15]FU M B.A Intrusion Detection System Based on Cluster Analysis.Software Engineering,2016,19(4):10-12.(in Chinese) 付明柏.一种基于聚类分析的入侵检测模型.软件工程,2016,19(4):10-12. [16]LI J,DENG G,LI H,et al.The relationship between similarity measure and entropy of intuitionistic fuzzy sets.Information Sciences,2012,188(1):314-321. [17]ASKARI S,MONTAZERIN N.A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering.Expert Systems with Applications,2015,42(9):2121-2135. |
[1] | 王馨彤, 王璇, 孙知信. 基于多尺度记忆残差网络的网络流量异常检测模型 Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network 计算机科学, 2022, 49(8): 314-322. https://doi.org/10.11896/jsjkx.220200011 |
[2] | 周志豪, 陈磊, 伍翔, 丘东亮, 梁广升, 曾凡巧. 基于SMOTE-SDSAE-SVM的车载CAN总线入侵检测算法 SMOTE-SDSAE-SVM Based Vehicle CAN Bus Intrusion Detection Algorithm 计算机科学, 2022, 49(6A): 562-570. https://doi.org/10.11896/jsjkx.210700106 |
[3] | 曹扬晨, 朱国胜, 孙文和, 吴善超. 未知网络攻击识别关键技术研究 Study on Key Technologies of Unknown Network Attack Identification 计算机科学, 2022, 49(6A): 581-587. https://doi.org/10.11896/jsjkx.210400044 |
[4] | 魏辉, 陈泽茂, 张立强. 一种基于顺序和频率模式的系统调用轨迹异常检测框架 Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns 计算机科学, 2022, 49(6): 350-355. https://doi.org/10.11896/jsjkx.210500031 |
[5] | 张师鹏, 李永忠. 基于降噪自编码器和三支决策的入侵检测方法 Intrusion Detection Method Based on Denoising Autoencoder and Three-way Decisions 计算机科学, 2021, 48(9): 345-351. https://doi.org/10.11896/jsjkx.200500059 |
[6] | 李贝贝, 宋佳芮, 杜卿芸, 何俊江. DRL-IDS:基于深度强化学习的工业物联网入侵检测系统 DRL-IDS:Deep Reinforcement Learning Based Intrusion Detection System for Industrial Internet of Things 计算机科学, 2021, 48(7): 47-54. https://doi.org/10.11896/jsjkx.210400021 |
[7] | 程希, 曹晓梅. 基于信息携带的SQL注入攻击检测方法 SQL Injection Attack Detection Method Based on Information Carrying 计算机科学, 2021, 48(7): 70-76. https://doi.org/10.11896/jsjkx.200600010 |
[8] | 戴宗明, 胡凯, 谢捷, 郭亚. 基于直觉模糊集的集成学习算法 Ensemble Learning Algorithm Based on Intuitionistic Fuzzy Sets 计算机科学, 2021, 48(6A): 270-274. https://doi.org/10.11896/jsjkx.200700036 |
[9] | 俞建业, 戚湧, 王宝茁. 基于Spark的车联网分布式组合深度学习入侵检测方法 Distributed Combination Deep Learning Intrusion Detection Method for Internet of Vehicles Based on Spark 计算机科学, 2021, 48(6A): 518-523. https://doi.org/10.11896/jsjkx.200700129 |
[10] | 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 |
[11] | 贾琳, 杨超, 宋玲玲, 程镇, 李琲珺. 改进的否定选择算法及其在入侵检测中的应用 Improved Negative Selection Algorithm and Its Application in Intrusion Detection 计算机科学, 2021, 48(6): 324-331. https://doi.org/10.11896/jsjkx.200400033 |
[12] | 王颖颖, 常俊, 武浩, 周详, 彭予. 基于WiFi-CSI的入侵检测方法 Intrusion Detection Method Based on WiFi-CSI 计算机科学, 2021, 48(6): 343-348. https://doi.org/10.11896/jsjkx.200700006 |
[13] | 郑嘉彤, 吴文渊. 基于MLWE的双向可否认加密方案 Practical Bi-deniable Encryption Scheme Based on MLWE 计算机科学, 2021, 48(3): 307-312. https://doi.org/10.11896/jsjkx.200100024 |
[14] | 刘全明, 李尹楠, 郭婷, 李岩纬. 基于Borderline-SMOTE和双Attention的入侵检测方法 Intrusion Detection Method Based on Borderline-SMOTE and Double Attention 计算机科学, 2021, 48(3): 327-332. https://doi.org/10.11896/jsjkx.200600025 |
[15] | 朱容辰, 李欣, 王晗旭, 叶瀚, 曹志威, 樊志杰. 融合多维标识特征的摄像头身份识别方法 Camera Identity Recognition Method Fused with Multi-dimensional Identification Features 计算机科学, 2021, 48(11A): 565-569. https://doi.org/10.11896/jsjkx.210100093 |
|