'
네트워크 데이터 셋의 클래스 불균형 해결을 위한 데이터 생성 및 분류 프레임워크 = Data generation and classification framework for resolving class imbalances in network data sets' 의 주제별 논문영향력
논문영향력 요약
주제
컴퓨터 프로그래밍,프로그램,자료
계층 어텐션 네트워크
데이터불균형
생성적 적대 신경망
인공신경망
인공지능
침입탐지시스템
동일주제 총논문수
논문피인용 총횟수
주제별 논문영향력의 평균
4,400
0
0.0%
주제별 논문영향력
논문영향력
주제
주제별 논문수
주제별 피인용횟수
주제별 논문영향력
주제분류(KDC/DDC)
컴퓨터 프로그래밍,프로그램,자 ...
970
0
0.0%
주제어
계층 어텐션 네트워크
1
0
0.0%
데이터불균형
13
0
0.0%
생성적 적대 신경망
49
0
0.0%
인공신경망
489
0
0.0%
인공지능
2,850
0
0.0%
침입탐지시스템
28
0
0.0%
계
4,400
0
0.0%
* 다른 주제어 보유 논문에서 피인용된 횟수
0
'
네트워크 데이터 셋의 클래스 불균형 해결을 위한 데이터 생성 및 분류 프레임워크 = Data generation and classification framework for resolving class imbalances in network data sets' 의 참고문헌
¡°kNN Approach to Unbalanced Data Distributions : ACase Study involving Information Extraction
pp . 42 ? 48[2003]
¡°TrainingCost-sensitive neural networks with methods addressing theClass imbalance problem
vol . 18 , no . 1 , pp . 63 ? 77[2006]
¡°Toward Generating a New Intrusion Detection Dataset and Intrusion TrafficCharacterization ,
pp . 108 ? 116[2018]
¡°Study on the Impact of Partition-Induced Dataset Shift on K-FoldCross-Validation ,
vol . 23 , no . 8 , pp . 1304 ? 1312[2012]
¡°Study of the impact of resampling methods forContrast pattern basedClassifiers in imbalanced databases
vol . 175 , pp . 935 ? 947[2016]
¡°SMOTEBoost : Improving Prediction of the MinorityClass in Boosting
¡± pp . 107 ? 119[2003]
¡°RecurrentConvolutional Neural Networks for TextClassi ?
vol . 333 , pp . 2267-2273[2015]
¡°RUSBoost : ImprovingClassification performance when training data is skewed
no . March 2016 , pp . 8 ? 11 ,[2008]
¡°Probabilistic framework of visual anomaly detection for unbalanced data
¡°On the importance of the validation technique forClassification with imbalanced datasets : AddressingCovariate shift when data is skewed
vol . 257 , pp . 1 ? 13[2014]
¡°MetaCost : a general method for makingClassifiersCost-sensitive , ¡± in Proceedings of the fifth ACM SIGKDD internationalConference on Knowledge discovery and data mining ? KDD ¡¯99
pp . 155 ? 164[1999]
¡°Hierarchical recurrent neural network for document modeling
pp . 899-907[2015]
¡°Hierarchical attention networks for documentClassi ?Cation
¡°Effective detection of sophisticated online banking fraud on extremely imbalanced data
vol . 16 , no . 4 , pp . 449 ? 475[2013]
¡°Borderline-SMOTE : A New Over-Sampling Method in , ¡±
pp . 878 ? 887[2005]
¡°Asymptotic Properties of Nearest Neighbor Rules Using Edited Data
vol . 2 , no . 3 , pp . 408 ? 421[1972]
¡°An insight intoClassification with imbalanced data : Empirical results andCurrent trends on using data intrinsicCharacteristics
vol . 250 , pp . 113 ? 141[2013]
¡°An empiricalComparison of botnet detection methods
vol . 45 , pp . 100 ? 123[2014]
¡°Addressing imbalance in multilabelClassification : Measures and random resampling algorithms ,
vol . 163 , pp . 3 ? 16[2015]
[UNB12] The UNB ISCX 2012 Intrusion Detection Evaluation Dataset. Available online: http://www.unb.ca/cic/research/datasets/ids.html (accessed on 23 February 2019).
[TOM76] I. Tomek, ¡°Two Modifications of CNN,¡± IEEE Trans. Syst. Man. Cybern., vol. SMC-6, no. 11, pp. 769?772, Nov. 1976.
[SHI17] S. Shilaskar, A. Ghatol, and P. Chatur, ¡°Medical decision support system for extremely imbalanced datasets,¡± Inf. Sci. (Ny)., vol. 384, pp. 205?219, Apr. 2017.
[RYA04] R. Ryan and A. Klautau, ¡°In Defense of One-Vs-All Classification,¡± Notes, vol. 7, pp. 101?141, 2004.
[PHA04] P. Phaal, ¡°sFlow Specification Version 5,¡± 2004. [Online]. Available: https://sflow.org/sflow_version_5.txt
[NKA14] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, ¡°A convolutional neural network for modelling sentences,¡± arXiv preprint arXiv:1404.2188, 2014.
[MOU15] N. Moustafa and J. Slay, ¡°UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems,¡± in Military Communications and Information Systems Conference (MilCIS). IEEE, 2015, pp. 1?6
[LAU01] J. Laurikkala, ¡°Improving identification of difficult small classes by balancing class distribution,¡± Proc. 8th Conf. AI Med. Eur. Artif. Intell. Med., pp. 63?66, 2001.
[KAN17] Kaggle. The state of data science and machine learning 2017, 2017. URL https://www. kaggle.com/surveys/2017.
https : //www . kaggle.com/surveys/2017[2017]
[JAP] N. Japkowicz, ¡°Assessment Metrics for Imbalanced Learning,¡± in Imbalanced Learning, Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013, pp. 187?206
[ISC19] ISCX VPN-nonVPN Encrypted Network Traffic Dataset. 2017. Available online: http://www.unb.ca/cic/ research/datasets/vpn.html (accessed on 23 February 2019).
[HAS98] T. Hastie and R. Tibshirani, ¡°Classification by pairwise coupling,¡± Ann. Stat., vol. 26, no. 2, pp. 451?471, 1998.
[CLA08] B. Claise, ¡°Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information,¡± RFC 5101, Internet Engineering Task Force, 2008
[CIS04] Cisco Systems NetFlow Services Export Version 9,¡± RFC 3954, Internet Engineering Task Force, 2004.
[BAT04] G. E. A. P. A. Batista, R. C. Prati, and M. C. Monard, ¡°A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data,¡± SIGKDD Explor. Newsl., vol. 6, no. 1, pp. 20?29, 2004.
[BAT03] G. E. A. P. A. Batista, A. L. C. Bazzan, and M. C. Monard, ¡°Balancing Training Data for Automated Annotation of Keywords: a Case Study,¡± Proc. Second Brazilian Work. Bioinforma., pp. 35?43, 2003.
Z. Session-Based Network Intrusion Detection Using a Deep Learning Architecture
Volume 10571 , pp . 144 ? 155[2017]
Z. End-to-end encrypted traffic classification with one-dimensional convolution neural networks
Towards the creation of synthetic , yet realistic , intrusion detection datasets
14
Toward developing a systematic approach to generate benchmark datasets for intrusion detection
[2012]
The Applications of Deep Learning on Traffic Identification
Available : http : //kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
R. S. A Review onClass Imbalance Problem : Analysis and Potential Solutions
14.6 : 43-51[2017]
Multiclass imbalance problems : Analysis and potential solutions
vol . 42 , no . 4 , pp . 1119 ? 1130[2012]
M. Deep Packet : A Novel Approach for Encrypted Traffic Classification Using Deep Learning
[2017]
Li X. Adversarial examples : attacks and defenses for deep learning .
.arXiv :[2017]
Learning from class-imbalanced data : Review of methods and applications .
73 : 220-239 .[2017]
Intrusion detection system using deep neural network for in-vehicle network security
[2016]
In : Advances in neural information processing systems
How to validate traffic generators ?
[2013]
How to Test an IDS ? : GENESIDS : An Automated System for Generating Attack Traffic .
[2018]
Flow-based network traffic generation using Generative Adversarial Networks
82 ([2019]
Flow-based benchmark data sets for intrusion detection
FLAME : a flow-level anomaly modeling engine . In : Proceedings of the workshop on cyber security experimentation and test ( CSET ) . USENIX Association ; 2008. p. 1:1 ? 1:6
FIXIDS : A high-speed signature-based flow intrusion detection system . NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium
[2018]
Exploratory study on class imbalance and solutions for network traffic classification
343 : 100-119 .[2019]
Effective data generation for imbalanced learning using conditional generative adversarial networks
91 (464-471 .[2018]
Detection of slow port scans in flow-based network traffic
Byte segment neural network for network traffic classification . 2018 IEEE/ACM 26th International Symposium on Quality of Service ( IWQoS )
[2018]
Braun T. A flow trace generator using graph-based traffic classification techniques
62 . doi : 10.1145/1815396.1815503 .
Analysing the classification of imbalanced data-sets with multiple classes : Binarization techniques and ad-hoc approaches
vol . 42 , pp . 97 ? 110[2013]
An instance level analysis of data complexity
vol . 95 , no . 2 , pp . 225 ? 256[2014]
ADASYN : Adaptive synthetic sampling approach for imbalanced learning
¡± no . 3 , pp . 1322 ? 1328[2008]
A.A. Toward developing a systematic approach to generate benchmark datasets for intrusion detection
31 , 357 ? 374 .[2012]
A detailed analysis of the kddcup 99 data set
pp . 1 ? 6[2009]
A deep learning based method for handling imbalanced problem in network traffic classification
[2017]
'
네트워크 데이터 셋의 클래스 불균형 해결을 위한 데이터 생성 및 분류 프레임워크 = Data generation and classification framework for resolving class imbalances in network data sets'
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