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 Paper › Conference Paper › peer-review

15 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	2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia
Publisher	IEEE
Pages	978-983
Number of pages	6
ISBN (Print)	9781509049059
DOIs	https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.340
Publication status	Published - 2017
Event	IEEE International Conference on Trust_Security and Privacy in Computing and Communications - Duration: 1 Aug 2017 → …

Conference

Conference	IEEE International Conference on Trust_Security and Privacy in Computing and Communications
Period	1/08/17 → …

Keywords

computational complexity
intrusion detection systems (computer security)
polynomials

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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 2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia (pp. 978-983). IEEE. 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. 2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia. IEEE, 2017. pp. 978-983

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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, intrusion detection systems (computer security), polynomials",

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

year = "2017",

doi = "10.1109/Trustcom/BigDataSE/ICESS.2017.340",

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booktitle = "2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia",

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note = "IEEE International Conference on Trust_Security and Privacy in Computing and Communications ; Conference date: 01-08-2017",

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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 2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia. IEEE, pp. 978-983, IEEE International Conference on Trust_Security and Privacy in Computing and Communications, 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.
2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia. IEEE, 2017. p. 978-983.

Research output: Chapter in Book / Conference Paper › Conference Paper › 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

Y1 - 2017

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 - intrusion detection systems (computer security)

KW - polynomials

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:44497

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BT - 2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia

PB - IEEE

T2 - IEEE International Conference on Trust_Security and Privacy in Computing and Communications

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Li Q, Tan Z, Jamdagni A, Nanda P, He X, Han W. An intrusion detection system based on polynomial feature correlation analysis. In 2017 IEEE Trustcom/BigDataSE/ICESS: Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, and the 14th IEEE International Conference on Embedded Software and Systems, 1-4 August 2017, Sydney, Australia. IEEE. 2017. p. 978-983 doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.340

An intrusion detection system based on polynomial feature correlation analysis

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