计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 173-179.doi: 10.11896/jsjkx.180801429
丁红卫, 万良, 周康, 龙廷艳, 辛壮
DING Hong-wei, WAN Liang, ZHOU Kang, LONG Ting-yan, XIN Zhuang
摘要： 当今网络数据呈现出更为庞大、复杂和多维的特性。传统的基于机器学习的方法在面临高维数据特征时需要手动提取大量特征,特征提取过程复杂且计算量大,达不到入侵检测的准确性和实时性的要求。深度学习在处理复杂数据方面具有较好的优势,可以自动从数据中提取更好的表示特征。为此,文中创新性地提出了一种基于深度卷积神经网络的入侵检测方法。首先,提出了一种将网络数据转换为图像的方法;然后,针对转换之后的图像设计了一个深度卷积神经网络模型,该模型使用两层的卷积层和池化层对图像进行降维处理,并引入了Relu函数作为新的非线性激活来代替传统的神经网络中常用的Sigmoid或Tanh函数,以加快网络的收敛速度,且该模型引入了Dropout方法来防止网络模型发生过度拟合的现象;最后,通过构建完成的深度卷积神经网络模型对转换之后的图像进行训练和识别。实验结果表明,与已有方法相比,所提方法具有更好的检测准确率、更低的误报率和更快的检测速率。
中图分类号:
| [1]ASHFAQ R A R,WANG X Z,HUANG Z X,et al.Fuzziness based semi-supervised learning approach forintrusion detection system[J].Information Sciences,2017,378(C):484-497. [2]QING S H,JIANG J C,MA H T,et al.Research on intrusion detection technique:a survey[J].Journal on Communications,2004,25(7):19-29.(in Chinese) 卿斯汉,蒋建春,马恒太,等.入侵检测技术研究综述[J].通信学报,2004,25(7):19-29. [3]ROY S S,MITTAL D,BASU A,et al.Stock market forecasting using LASSO linear regression model[C]//Afro-European Conference for Industrial Advancement.Cham:Springer,2015:371-381. [4]BASU A,ROY S S,ABRAHAM A.A Novel Diagnostic Approach Based on SupportVector Machine with Linear Kernel for Classifying the Erythemato-Squamous Disease[C]//InternationalConference on Computing Communication Controland Automation.New York:IEEE Press,2015:343-347. [5]ROY S S,VISWANATHAM V M.Classifying Spam Emails Using Artificial Intelligent Techniques[J].International Journal of Engineering Research in Africa,2016,22:152-161. [6]TAN B,TAN Y,LI Y.Research on Intrusion Detection System Based on Improved PSO-SVM Algorithm[J].Chemical Engineering Transaction,2016,51:583-588. [7]MITTAL D,GAURAV D,ROY S S.An effective hybridized-classifier for breast cancer diagnosis[C]//IEEE International Conference on Advanced Intelligent Mechatronics.New York:IEEE Press,2015:1026-1031. [8]HINTON G E,SALAKHUTDINOV R R.Reducing the Dimen-sionality of Data with Neural Networks[J].Science,2006,313(5786):504-507. [9]ROY S S,MALLIK A,GULATI R,et al.A deep learning based artificial neural network approach for intrusion detection[C]//International Conference on Mathematics and Computing.Singapore:Springer,2017:44-53. [10]YIN C,ZHU Y,FEI J,et al.A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks[J].IEEE Access,2017,5(99):21954-21961. [11]ALOM M Z,BONTUPALLI V R,TAHA T M.Intrusion detection using deep belief networks[C]//Aerospace and Electronics Conference.New York:IEEE Press,2016:339-344. [12]JAVAID A,NIYAZ Q,SUN W,et al.A Deep Learning Ap-proach for Network Intrusion Detection System[C]//Eai International Conference on Bio-Inspired Information and Communications Technologies.Pittsburgh:ICST,2016:21-26. [13]POTLURI S,DIEDRICH C.Accelerated deep neural networks for enhanced Intrusion Detection System[C]//IEEE,International Conference on Emerging Technologies and Factory Automation.New York:IEEE Press,2016. [14]YU Y,LONG J,CAI Z.Session-Based Network IntrusionDetection Using a Deep Learning Architecture[M]//Modeling Decisions for Artificial Intelligence.Berlin:Springer Netherlands,2017:144-155. [15]KWON D,KIM H,KIM J,et al.A survey of deep learning-based network anomaly detection[J].Cluster Computing,2017(5):1-13. [16]WANG M,LI J.Network Intrusion Detection Model Based on Convolutional Neural Network[J].Journal of Information Security Research,2017,3(11):990-994.(in Chinese) 王明,李剑.基于卷积神经网络的网络入侵检测系统[J].信息安全研究,2017,3(11):990-994. [17]TAVALLAEE M,BAGHERI E,LU W,et al.A detailed analysis of the KDD CUP 99 data set[C]//IEEE International Conference on Computational Intelligence for Security & Defense Applications.New York:IEEE,2009:1-6. [18]SZARVAS M,YOSHIZAWA A,YAMAMOTO M,et al.Pedestrian detection with convolutional neural networks[C]//Intelligent Vehicles Symposium.New York:IEEE Press,2005:224-229. [19]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.New York:IEEE Press,2012:1097-1105. [20]ZHANG Y L,ZHANG Z Q,WU H T,et al.Perimeter intrusion detection method based on improved convolution neural network[J].Computer Science,2017,44(3):182-186.(in Chinese) 张永良,张智勤,吴鸿韬,等.基于改进卷积神经网络的周界入侵检测方法[J].计算机科学,2017,44(3):182-186. [21]CHEN L,QU H,ZHAO J,et al.Efficient and robust deep lear-ning with Correntropy-induced loss function[J].Neural Com-puting & Applications,2016,27(4):1019-1031. [22]SADEK R A,SOLIMAN M S,ELSAYED H S.Effective Anoma-ly Intrusion Detection System based on Neural Network with Indicator Variable and Rough set Reduction[J].International Journal of Computer Science Issues,2013,10(6):227-233. [23]KUANG F,XU W,ZHANG S.A novel hybrid KPCA and SVM with GA model for intrusion detection[J].Applied Soft Computing Journal,2014,18(C):178-184. [24]YIN C,ZHU Y,FEI J,et al.A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks[J].IEEE Access,2017,5(99):21954-21961. [25]GAO N,GAO L,HE Y Y.Deep belief nets model oriented to intrusion detection system[J].Systems Engineering and Electro-nices,2016,38(9):2201-2207.(in Chinese) 高妮,高岭,贺毅岳.面向入侵检测系统的Deep Belief Nets模型[J].系统工程与电子技术,2016,38(9):2201-2207. |
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