基于深度卷积神经网络的SQL注入攻击检测
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摘要
结合自然语言处理技术,采用卷积神经网络算法训练SQL注入检测模型,主要包括文本分词处理、提取文本向量和训练检测模型三个部分。实验结果与BP神经网络算法结果对比,发现基于卷积神经网络的SQL注入检测模型仅需提取用户输入的信息,就可以对攻击行为进行检测,具有很强的预测能力,同时针对变异SQL注入攻击具有良好的识别能力。
This paper combines natural language processing technology and uses convolution neural network algorithm to train SQL injection detection model.It includes three parts:text segmentation processing,extracting text vectors and training detection models.By comparing the BP neural network algorithm,the experimental results show that the SQL injection detection model based on the convolution neural network only needs to extract the information from the user input,and can detect the attack behavior,which has a strong prediction ability and is good for the variant SQL injection attack with clockwise.At the same time,it has good recognition ability against variant SQL injection attacks.
This paper combines natural language processing technology and uses convolution neural network algorithm to train SQL injection detection model.It includes three parts:text segmentation processing,extracting text vectors and training detection models.By comparing the BP neural network algorithm,the experimental results show that the SQL injection detection model based on the convolution neural network only needs to extract the information from the user input,and can detect the attack behavior,which has a strong prediction ability and is good for the variant SQL injection attack with clockwise.At the same time,it has good recognition ability against variant SQL injection attacks.
引文
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[2]Open Web Application Security Project(OWASP):2017 OWASP Top Ten[EB/OL][2017-10-25].http://www.owasp.org.cn/owasp-project/OWASPTop102017v1.3.pdf.
[3]JUSTIN CLARKE.SQL注入攻击与防御[M].2版.北京:清华大学出版社,2013.
[4]ANTUNES J,NEVES N,CORREIA M,et al.Vulnerability discovery with attack injection[J].IEEE Transactions on Software Engineering,2010,36(3):357-370.
[5]AHMED M,MAHMOOD A N,HU J.A survey of network anomaly detection techniques[J].Journal of Network & Computer Applications,2016,60:19-31.
[6]VALDES A,SKINNER K.Adaptive,model-based monitoring for cyber attack detection[C]//International Workshop on Recent Advances in Intrusion Detection.Berlin,Heidelberg:Springer,2000:80-93.
[7]李红灵,邹建鑫.基于SVM和文本特征向量提取的SQL注入检测研究[J].信息网络安全,2017(12):40-46.
[8]杨连群,孟魁,王斌,等.基于隐马尔可夫模型的新型SQL注入攻击检测方法[J].信息网络安全,2017(9):115-118.
[9]张志超,王丹,赵文兵,等.一种基于神经网络的SQL注入漏洞的检测模型[J].计算机与现代化,2016(10):67-71.
[10]陈先昌.基于卷积神经网络的深度学习算法与应用研究[D].杭州:浙江工商大学,2014.
[11]苑兆忠,姜华.Web挖掘技术在信息检索中的应用研究[J].聊城大学学报(自然科学版),2006,19(1):74-77.
[12]林晓佳.基于改进Adaboost M1算法医学图像分类系统的研究[J].聊城大学学报(自然科学版),2015,28(4):29-32.
[13]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convloutional neural networks[C]//Advances in Neural Information Processing Systems.Massachusetts:The MIT Press,2012:1097-1105.
[14]SIMARD P,STEINKRAUS D,PLATT J C.Best practices for convolational neural networks applied to visual document analysis[C]//7th International Conference on Document Analysis and Recognition(ICDAR 2003).Washington D C:IEEE Computer Society,2003(3):958-962.
[15]CLARK C,STORKEY A,CLARK C,et al.Teaching deep convolutional neural networks to play go[J].Eprint Arxiv,2014:1766-1774.
[16]蒋磊.基于机器学习的SQL注入检测技术研究[D].南京:南京邮电大学,2017.
[17]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[C/OL]//Proceedings of Workshop at ICLR,2013.[2018-06-03]http://www.researchgate.net/publication/234131319_Eifficent_Estimation_of_word_Representations_in_Vector_Space.
[18]HU B,LU Z,LI H,et al.Convolutional neural network architectures for matching natural language sentences[C]//Advances in Neural Information Processing Systems.Massachusetts:The MIT Press,2014:2042-2050.