One intrusion detection method based on uniformed conditional dynamic mutual information

Liangfu Lu; Xinhe Zhu; Xuyun Zhang; Junhan Liu; Md Zakirul Alam Bhuiyan; Guangtai Cui

doi:10.1109/TrustCom/BigDataSE.2018.00170

One intrusion detection method based on uniformed conditional dynamic mutual information

Liangfu Lu, Xinhe Zhu, Xuyun Zhang^*, Junhan Liu, Md Zakirul Alam Bhuiyan, Guangtai Cui

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

Abstract

With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.

Original language	English
Title of host publication	Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1236-1241
Number of pages	6
ISBN (Electronic)	9781538643884
ISBN (Print)	9781538643877, 9781538643891
DOIs	https://doi.org/10.1109/TrustCom/BigDataSE.2018.00170
Publication status	Published - 5 Sept 2018
Externally published	Yes
Event	17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 - New York, United States Duration: 31 Jul 2018 → 3 Aug 2018

Conference

Conference	17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Country/Territory	United States
City	New York
Period	31/07/18 → 3/08/18

Keywords

Feature Selection
Intrusion Detection
Mutual Information
SVM

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10.1109/TrustCom/BigDataSE.2018.00170

Cite this

Lu, L., Zhu, X., Zhang, X., Liu, J., Bhuiyan, M. Z. A., & Cui, G. (2018). One intrusion detection method based on uniformed conditional dynamic mutual information. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 (pp. 1236-1241). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/TrustCom/BigDataSE.2018.00170

Lu, Liangfu ; Zhu, Xinhe ; Zhang, Xuyun et al. / One intrusion detection method based on uniformed conditional dynamic mutual information. Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers (IEEE), 2018. pp. 1236-1241

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abstract = "With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.",

keywords = "Feature Selection, Intrusion Detection, Mutual Information, SVM",

author = "Liangfu Lu and Xinhe Zhu and Xuyun Zhang and Junhan Liu and Bhuiyan, {Md Zakirul Alam} and Guangtai Cui",

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booktitle = "Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Lu, L, Zhu, X, Zhang, X, Liu, J, Bhuiyan, MZA & Cui, G 2018, One intrusion detection method based on uniformed conditional dynamic mutual information. in Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers (IEEE), pp. 1236-1241, 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018, New York, United States, 31/07/18. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00170

One intrusion detection method based on uniformed conditional dynamic mutual information. / Lu, Liangfu; Zhu, Xinhe; Zhang, Xuyun et al.
Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 1236-1241.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - One intrusion detection method based on uniformed conditional dynamic mutual information

AU - Lu, Liangfu

AU - Zhu, Xinhe

AU - Zhang, Xuyun

AU - Liu, Junhan

AU - Bhuiyan, Md Zakirul Alam

AU - Cui, Guangtai

PY - 2018/9/5

Y1 - 2018/9/5

N2 - With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.

AB - With the rapid development of our society, World Wide Web has turned to be an indispensible part of our daily life. Meanwhile, the network security is becoming more and more important. Intrusion Detection System (IDS), which serves to detect the abnormal activities in computers and internet, is often used to solve the network security problems. However, the IDS has to face and process the high dimensional data with high redundancy due to the increasing scale and dimension of the data, which causes the low efficiency of IDS. This paper proposes a new feature selection method for intrusion detection based on the Uniformed Conditional Dynamic Mutual Information (UCDMIFS), which can highly decrease the dimensionality and increase the detection accuracy. To examine our algorithm, the UCDMIFS algorithm is applied to the KDD Cup 99 data set and compared with other algorithms, such as support vector machine (SVM), to detect the intrusions. The experiments illustrate the efficiency of our algorithm.

KW - Feature Selection

KW - Intrusion Detection

KW - Mutual Information

KW - SVM

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DO - 10.1109/TrustCom/BigDataSE.2018.00170

M3 - Conference proceeding contribution

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SN - 9781538643891

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BT - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018

Y2 - 31 July 2018 through 3 August 2018

ER -

Lu L, Zhu X, Zhang X, Liu J, Bhuiyan MZA, Cui G. One intrusion detection method based on uniformed conditional dynamic mutual information. In Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers (IEEE). 2018. p. 1236-1241 doi: 10.1109/TrustCom/BigDataSE.2018.00170

One intrusion detection method based on uniformed conditional dynamic mutual information

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