Computer Science ›› 2021, Vol. 48 ›› Issue (6): 246-252.doi: 10.11896/jsjkx.201200142
• Computer Network • Previous Articles Next Articles
XU Jia-qing, HU Xiao-yue, TANG Fu-qiao, WANG Qiang, HE Jie
CLC Number:
[1]LAPTEV N,AMIZADEH S,FLINT I.Generic and scalableframework forautomated time-series anomaly detection[C]//Proceedings of the 21th ACM SIGKDD International Confe-rence on Knowledge Discovery and Data Mining.2015:1939-1947. [2]LIU D,ZHAO Y,XU H,et al.Opprentice:Towards practical and automatic anomaly detection through machine learning[C]//Proceedings of the 2015 Internet Measurement Confe-rence.2015:211-224. [3]CHALAPATHY R,BORZESHI E Z,PICCARDI M.An investigation of recurrent neural architectures for drug name recognition[J].arXiv:1609.07585,2016. [4]CHEN S,PENG M,XIONG H,et al.SVM intrusion detection model based on compressed sampling[J].Journal of Electrical and Computer Engineering,2016,2016(pt.1):12. [5]JIANG J,JING X,LV B,et al.A Novel Multi-classification Intrusion Detection Model Based on Relevance Vector Machine[C]//2015 11th International Conference on Computational Intelligence and Security (CIS).IEEE,2015:303-307. [6]HASSANZADEH R,NAYAK R.A semi-supervised graph-based algorithm for detecting outliers in online-social-networks[C]//Proceedings of the 28th Annual ACM Symposium on Applied Computing.2013:577-582. [7]RUFF L,VANDERMEULEN R A,GÖRNITZ N,et al.Deep semi-supervised anomaly detection[J].arXiv:1906.02694,2019. [8]PANG G,HENGEL A,SHEN C.Weakly-supervised DeepAnomaly Detection [J].arXiv:1910.13601v2,2020. [9]AMER M,GOLDSTEIN M,ABDENNADHER S.Enhancingone-class support vectormachines for unsupervised anomaly detection[C]//Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description.2013:8-15. [10]MÜNZ G,LI S,CARLE G.Traffic anomaly detection using k-means clustering[C]//GI/ITG-Workshop MMBnet 2007. [11]STEFANIDIS K,VOYIATZIS A G.An HMM-based anomaly detection approach for SCADA systems[C]//IFIP International Conference on Information Security Theory and Practice.Cham:Springer,2016:85-99. [12]ZHANG C,SONG D,CHEN Y,et al.A deep neural network for unsupervised anomaly detection and diagnosis in multivariatetime series data[C]//Proceedings of the AAAI Conference onArtificial Intelligence.2019,33:1409-1416. [13]XU H,CHEN W,ZHAO N,et al.Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications[C]//Proceedings of the 2018 World Wide Web Confe-rence.2018:187-196. [14]ZENATI H,FOO C S,LECOUAT B,et al.Efficient gan-based anomaly detection[J].arXiv:1802.06222,2018. [15]CAO J J,XIAO L Q,WANG K F,et al.Implementation andevaluation of in-band management of supercomputing system interconnection network[J].Journal of Computers,2015,38:119. |
[1] | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49. |
[2] | 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. |
[3] | WANG Wen-qiang, JIA Xing-xing, LI Peng. Adaptive Ensemble Ordering Algorithm [J]. Computer Science, 2022, 49(6A): 242-246. |
[4] | QUE Hua-kun, FENG Xiao-feng, LIU Pan-long, GUO Wen-chong, LI Jian, ZENG Wei-liang, FAN Jing-min. Application of Grassberger Entropy Random Forest to Power-stealing Behavior Detection [J]. Computer Science, 2022, 49(6A): 790-794. |
[5] | ZHANG Xiao-qing, FANG Jian-sheng, XIAO Zun-jie, CHEN Bang, Risa HIGASHITA, CHEN Wan, YUAN Jin, LIU Jiang. Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image [J]. Computer Science, 2022, 49(3): 204-210. |
[6] | LIU Zhen-yu, SONG Xiao-ying. Multivariate Regression Forest for Categorical Attribute Data [J]. Computer Science, 2022, 49(1): 108-114. |
[7] | YANG Xiao-qin, LIU Guo-jun, GUO Jian-hui, MA Wen-tao. Full Reference Color Image Quality Assessment Method Based on Spatial and Frequency Domain Joint Features with Random Forest [J]. Computer Science, 2021, 48(8): 99-105. |
[8] | ZHENG Jian-hua, LI Xiao-min, LIU Shuang-yin, LI Di. Improved Random Forest Imbalance Data Classification Algorithm Combining Cascaded Up-sampling and Down-sampling [J]. Computer Science, 2021, 48(7): 145-154. |
[9] | CAO Yang-chen, ZHU Guo-sheng, QI Xiao-yun, ZOU Jie. Research on Intrusion Detection Classification Based on Random Forest [J]. Computer Science, 2021, 48(6A): 459-463. |
[10] | LI Na-na, WANG Yong, ZHOU Lin, ZOU Chun-ming, TIAN Ying-jie, GUO Nai-wang. DDoS Attack Random Forest Detection Method Based on Secondary Screening of Feature Importance [J]. Computer Science, 2021, 48(6A): 464-467. |
[11] | ZHOU Yi-min, LIU Fang-zheng , WANG Yong. IPSec VPN Encrypted Traffic Identification Based on Hybrid Method [J]. Computer Science, 2021, 48(4): 295-302. |
[12] | ZHANG Tian-rui, WEI Ming-qi, GAO Xiu-xiu. Prediction Model of Bubble Dissolution Time in Selective Laser Sintering Based on IPSO-WRF [J]. Computer Science, 2021, 48(11A): 638-643. |
[13] | LIU Zhen-peng, SU Nan, QIN Yi-wen, LU Jia-huan, LI Xiao-fei. FS-CRF:Outlier Detection Model Based on Feature Segmentation and Cascaded Random Forest [J]. Computer Science, 2020, 47(8): 185-188. |
[14] | YANG Wei-chao, GUO Yuan-bo, LI Tao, ZHU Ben-quan. Method Based on Traffic Fingerprint for IoT Device Identification and IoT Security Model [J]. Computer Science, 2020, 47(7): 299-306. |
[15] | WANG Xiao-hui, ZHANG Liang, LI Jun-qing, SUN Yu-cui, TIAN Jie, HAN Rui-yi. Study on XGBoost Improved Method Based on Genetic Algorithm and Random Forest [J]. Computer Science, 2020, 47(11A): 454-458. |
|