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
N1 - Publisher Copyright:
© 2017 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85032383951&partnerID=8YFLogxK
U2 - 10.1109/Trustcom/BigDataSE/ICESS.2017.340
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 -
