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AutoEncoder와 CGAN을 이용한 딥러닝 기반의 고성능 네트워크 침입탐지 시스템 = High Performance Network Intrusion Detection System Based on Deep Learning Using AutoEncoder and CGAN

이주화 2020년

논문상세정보
인용/피인용
' AutoEncoder와 CGAN을 이용한 딥러닝 기반의 고성능 네트워크 침입탐지 시스템 = High Performance Network Intrusion Detection System Based on Deep Learning Using AutoEncoder and CGAN' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • GAN
  • auto encoder
  • 딥 러닝
  • 침입탐지시스템
  • 특징 추출
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
2,172 0

0.0%

' AutoEncoder와 CGAN을 이용한 딥러닝 기반의 고성능 네트워크 침입탐지 시스템 = High Performance Network Intrusion Detection System Based on Deep Learning Using AutoEncoder and CGAN' 의 참고문헌

  • 이주화, 박기현, “접근이 어려운 IOT 환경에서의 IDS를 위한 효과적인 특징 추출과 분류”
    년 정보처리학회 춘계학술발표대회 논문집, 제 26권, 제1호, pp. 714-717, 5월, 2019 [2019]
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    Vol . 2 , pp . 1-4
  • [35] Intrusion detection evaluation dataset (ISCXIDS2012), https://www.unb.ca/cic/datasets/ids.html, cited 2018 April 28.
  • [20] Denning, D. E., ‘‘An intrusion-detection model,’’ IEEE Trans. on Software Engineering, Vol. SE-13, No. 2, pp. 222?232, February, 1987.
    Vol . SE-13 , No . 2 , pp . 222 ? 232
  • [16] Intrusion Detection Evaluation Dataset (CICIDS2017), https://www. unb.ca/cic/datasets/ids-2017.html, cited 2018 October 07.
  • [15] The UNSW-NB15 Dataset Description, https://www.unsw.adfa. edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/, cited 2019 February 15.
  • [14] NSL-KDD Dataset, https://www.unb.ca/cic/datasets/nsl.html, cited 2018 March 20.
  • [13] KDD Cup 1999 Data, http://kdd.ics.uci.edu/databases/kddcup99/ kddcup99.html, cited 2018 March 20.
  • Using convolutional neural networks to network intrusion detection for cyber threats
    pp . 1107-1110
  • Using Different Cost Functions to Train Stacked Auto-Encoders ,
    pp . 114-120 ,
  • Unrolled Generative Adversarial Networks
    pp . 1-25
  • Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization ,
    pp . 108-116
  • The cubicle vs. the coffee shop : Behavioral modes in enterprise end-users , ” P assive and Active Measurement Conf.
    Vol . 4979 , pp . 202-211 ,
  • Survey on Host and Network Based Intrusion Detection System
    Vol . 6 , No . 2 , pp . 2266-2269
  • Selection ofCandidate support vectors in incremental SVM for network intrusion detection
    Vol 45 , pp . 231-241
  • SUPPORT VECTOR MACHINE-A Survey
    Vol . 2 , No . 8 , pp . 82-85
  • SMOTE-NCL : A re-sampling method with filter for network intrusion detection
    pp . 1157-1161
  • SMOTE : Synthetic Minority Over-sampling Technique ,
    Vol . 16 , pp . 321-357 ,
  • Performance Comparison of Support Vector Machine , Random Forest , and Extreme Learning Machine for Intrusion Detection
    Vol . 6 , pp . 33789-33795
  • Overfitting problem and the over-training in the era of data : Particularly for Artificial Neural Networks
    pp . 173-177
  • Network intrusion detection system using feature extraction based on AutoEncoder in IOT environment
    Vol . 8 , No . 12 , pp . 483-490 ,
  • Network intrusion detection system using J48 Decision Tree
    pp . 2023-2026
  • Network Intrusuion Detection based on LDA for payload feature selection
    pp . 1545-1549
  • Minimal dataset for Network Intrusion Detection Systems via dimensionality reduction ,
    pp . 168-173
  • Learning Automata based Feature Selection for Network Traffic Intrusion Detection
    pp . 622-627
  • Intrusion detection using deep belief networks
    pp . 339-344
  • Intrusion detection based on K-Means clustering and Naive Bayes classification , ” 2011 7th Int . Conf . on Information Technology in Asia
    pp . 1-6 ,
  • Intrusion and misuse detection in large-scale systems
    Vol . 22 , No . 1 , pp . 38-48 ,
  • Intrusion Detection in Network Systems Through Hybrid Supervised and Unsupervised Machine Learning Process : A Case Study on the ISCX Dataset
    pp . 219-226
  • Intrusion Detection System Using Data Mining Technique : Support Vector Machine
    Vol . 3 , No . 3 , pp . 581-586
  • Intrusion Detection Based on IDBM , ” 14th Int . Conf . on Pervasive Intelligence and Computing , 2nd Int . Conf . on Big Data Intelligence and Computing and Cyber Science and Technology Congress
    pp . 173-177
  • Information-theoretic measures for anomaly detection
    pp . 130-143
  • Independent Component Analysis via Distance Covariance
    Vol . 112 , No . 518 , pp . 623-637
  • Improving Intrusion Detection System Based on KNN and KNN-DS with detection of U2R , R2L attack for Network Probe Attack Detection
    Vol . 2 , No . 5 , pp . 209-212 , [2016]
  • Improvement of network intrusion detection accuracy by using restricted Boltzmann machine , “ 2016 8th Int . Conf . on Computational Intelligence and Communication Networks
    pp . 413 ? 417
  • Importance of input data normalization for the application of neural networks to complex industrial problems
    Vol 44 , No 3 , pp . 1464-1468
  • IDS in Telecommunication Network Using PCA
    Vol . 5 , No . 4 , pp . 147-157
  • Hybrid Intrusion Detection : Combining Decision Tree and Gaussian Mixture Model ,
    pp . 8-12
  • HAST-IDS : Learning Hierarchical SpatialTemporal Features using Deep Neural Networks to Improve Intrusion Detection
    Vol . 6 , pp . 1792-1806 ,
  • Gradient-based learning applied to document recognition
    Vol . 86 , No . 11 , pp . 2278-2324 ,
  • Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems
    pp . 78-83
  • Generative Adversarial Nets
    pp . 2672-2680
  • Feature s Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection
    Vol . 8 , No . 3 , p p. 1-27
  • Feature Selection : Evaluation , Application , and Small Sample Performance
    Vol . 19 , No . 2 , pp . 153-158
  • Feature Selection : A Data Perspective
    Vol . 50 , No . 6 , pp . 1-45
  • Efficient learning of deep Boltzmann machines
    pp . 693-700
  • Efficient Feature Extraction Using Apache Spark for Network Behavior Anomaly Detection
    Vol . 23 , No . 5 , pp . 561-573
  • Effective Feature Extraction via Stacked Sparse AutoEncoder to Improve Intrusion Detection System
    Vol . 6 , pp . 41238-41248
  • Effective Discriminant Function for Intrusion Detection Using SVM
    pp . 1148-1153
  • Distributed Anomaly Detection using AE Neural Networks in WSN for IoT
    pp . 1-6
  • Dimension Reduction With Extreme Learning Machine
    Vol . 25 , No . 8 , pp . 3906-3918
  • Deep Learning Approach Combining Sparse AE With SVM for Network Intrusion Detection
    Vol . 6 , pp . 52843-52856
  • Comparison of Kullback-Leibler divergence approximation methods between Gaussian mixture models for satellite image retrieval ,
    pp . 3719-3722
  • Combining Bagging and Random Subspaces to Create Better Ensembles ,
    Vol . 4723 , pp . 118-129
  • Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm
    pp . 282-286
  • Benchmarking datasets for Anomaly-based Network Intrusion Detection : KDD CUP 99 alternatives
    pp . 1-8
  • Automatic feature selection for anomaly detection
    pp . 71-76
  • Assessing Generative Models via Precision and Recall ,
    pp . 1-10
  • An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection
    Vol . 144 , pp . 111-119
  • An approach to implement a network intrusion detection system using genetic algorithms ,
    pp . 221-228
  • An Evaluation Framework for Intrusion Detection Dataset ,
    pp . 1-6
  • An Application of Oversampling , Undersampling , Bagging and Boosting in Handling Imbalanced Datasets
    Vol . 285 , pp . 13-22
  • AE-CGAN Model based High Performance Network Intrusion Detection System
    Vol . 9 , No . 20 , pp . 1-14 ,
  • A survey of network anomaly detection techniques
    Vol . 60 , pp . 19-31 ,
  • A survey of feature selection and feature extraction techniques in machine learning
    pp . 372-378
  • A survey of deep learning methods and software tools for image classification and object detection
    Vol 26 , No . 1 , pp . 9-15
  • A novel region adaptive SMOTE algorithm for intrusion detection on imbalanced problem
    pp . 1281-1286
  • A Survey on Machine Learning Techniques for Intrusion Detection Systems
    Vol . 2 , No . 11 , pp . 4349-4355
  • A Survey of Random Forest Based Methods for Intrusion Detection Systems
    Vol . 51 , No . 48 , pp . 1-36 ,
  • A Study on the Node Split in Decision Tree with Multivariate Target Variables
    Vol . 13 , No . 4 , pp . 386-390
  • A Feature Set Selection Approach Based on Pearson Correlation Coefficient for Real Time Attack Detection
    Vol . 18 , pp . 59-66
  • A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
    Vol . 5 , pp . 21954-21961
  • A Comparative Performance Evaluation of Intrusion Detection based on Neural Network and PCA
    pp . 841-845