An intrusion detection system based on polynomial feature correlation analysis

Qingru Li; Zhiyuan Tan; Aruna Jamdagni; Priyadarsi Nanda; Xiangjian He; Wei Han

doi:10.1109/Trustcom/BigDataSE/ICESS.2017.340

An intrusion detection system based on polynomial feature correlation analysis

Qingru Li
, Zhiyuan Tan
, Aruna Jamdagni
, Priyadarsi Nanda
, Xiangjian He
, Wei Han

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

16 Citations (Scopus)

Abstract

This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.

Original language	English
Title of host publication	Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	978-983
Number of pages	6
ISBN (Electronic)	9781509049059
DOIs	https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.340
Publication status	Published - 7 Sept 2017
Externally published	Yes
Event	16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017 - Sydney, Australia Duration: 1 Aug 2017 → 4 Aug 2017

Publication series

Name	Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017

Conference

Conference	16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017
Country/Territory	Australia
City	Sydney
Period	1/08/17 → 4/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Free Keywords

Computational complexity
Feature correlation analysis
Intrusion Detection System (IDS)
Mahalanobis distance
Polynomial

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Software
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/Trustcom/BigDataSE/ICESS.2017.340

Cite this

Li, Q., Tan, Z., Jamdagni, A., Nanda, P., He, X., & Han, W. (2017). An intrusion detection system based on polynomial feature correlation analysis. In Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017 (pp. 978-983). Article 8029543 (Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.340

Li, Qingru ; Tan, Zhiyuan ; Jamdagni, Aruna et al. / An intrusion detection system based on polynomial feature correlation analysis. Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 978-983 (Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017).

@inproceedings{e44e24c620484939bf7309316ed4da9f,

title = "An intrusion detection system based on polynomial feature correlation analysis",

abstract = "This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.",

keywords = "Computational complexity, Feature correlation analysis, Intrusion Detection System (IDS), Mahalanobis distance, Polynomial",

author = "Qingru Li and Zhiyuan Tan and Aruna Jamdagni and Priyadarsi Nanda and Xiangjian He and Wei Han",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017 ; Conference date: 01-08-2017 Through 04-08-2017",

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pages = "978--983",

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}

Li, Q, Tan, Z, Jamdagni, A, Nanda, P, He, X & Han, W 2017, An intrusion detection system based on polynomial feature correlation analysis. in Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017., 8029543, Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017, Institute of Electrical and Electronics Engineers Inc., pp. 978-983, 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017, Sydney, Australia, 1/08/17. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.340

An intrusion detection system based on polynomial feature correlation analysis. / Li, Qingru; Tan, Zhiyuan; Jamdagni, Aruna et al.
Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 978-983 8029543 (Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017).

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

TY - GEN

T1 - An intrusion detection system based on polynomial feature correlation analysis

AU - Li, Qingru

AU - Tan, Zhiyuan

AU - Jamdagni, Aruna

AU - Nanda, Priyadarsi

AU - He, Xiangjian

AU - Han, Wei

PY - 2017/9/7

Y1 - 2017/9/7

N2 - This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.

AB - This paper proposes an anomaly-based Intrusion Detection System (IDS), which flags anomalous network traffic with a distance-based classifier. A polynomial approach was designed and applied in this work to extract hidden correlations from traffic related statistics in order to provide distinguishing features for detection. The proposed IDS was evaluated using the well-known KDD Cup 99 data set. Evaluation results show that the proposed system achieved better detection rates on KDD Cup 99 data set in comparison with another two state-of-the-art detection schemes. Moreover, the computational complexity of the system has been analysed in this paper and shows similar to the two state-of-the-art schemes.

KW - Computational complexity

KW - Feature correlation analysis

KW - Intrusion Detection System (IDS)

KW - Mahalanobis distance

KW - Polynomial

UR - https://www.scopus.com/pages/publications/85032383951

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DO - 10.1109/Trustcom/BigDataSE/ICESS.2017.340

M3 - Conference contribution

AN - SCOPUS:85032383951

T3 - Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017

SP - 978

EP - 983

BT - Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017

Y2 - 1 August 2017 through 4 August 2017

ER -

Li Q, Tan Z, Jamdagni A, Nanda P, He X, Han W. An intrusion detection system based on polynomial feature correlation analysis. In Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 978-983. 8029543. (Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017). doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.340

An intrusion detection system based on polynomial feature correlation analysis

Abstract

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Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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