Computer Science ›› 2019, Vol. 46 ›› Issue (10): 173-179.doi: 10.11896/jsjkx.180801429
• Information Security • Previous Articles Next Articles
DING Hong-wei, WAN Liang, ZHOU Kang, LONG Ting-yan, XIN Zhuang
CLC Number:
[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. |
[1] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[2] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
[3] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
[4] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
[5] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
[6] | WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322. |
[7] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[8] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
[9] | ZHANG Yuan, KANG Le, GONG Zhao-hui, ZHANG Zhi-hong. Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM [J]. Computer Science, 2022, 49(7): 31-39. |
[10] | HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78. |
[11] | ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105. |
[12] | ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112. |
[13] | CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126. |
[14] | HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163. |
[15] | ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169. |
|