Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function-Reference-Cited by-同舟云学术

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Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fourth International Conference on Engineering & MIS 2018 - ICEMIS '18
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Author:

Kumar Gunupudi Rajesh¹,Mangathayaru Nimmala¹,Narsimha Gugulothu²,Cheruvu Aravind³

Affiliation:

1. Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India

2. Department of CSE, JNTU, College of Engineering, Sultanpur, Hyderabad, India

3. Department of Information Techology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India

Publisher

ACM Press

Reference44 articles.

1. Mohiuddin Ahmed, Abdun Naser Mahmood, and Jiankun Hu. 2016. A survey of network anomaly detection techniques. Journal of Network and Computer Applications 60 (2016), 19--31. https://doi.org/10.1016/j.jnca.2015.11.016

2. Omar Y. Al-Jarrah, Yousof Al-Hammdi, Paul D. Yoo, Sami Muhaidat, and Mahmoud Al-Qutayri. 2017. Semi-Supervised Multi-Layered Clustering Model for Intrusion Detection. Digital Communications and Networks (2017). https://doi.org/10.1016/j.dcan.2017.09.009

3. Wathiq Laftah Al-Yaseen, Zulaiha Ali Othman, and Mohd Zakree Ahmad Nazri. 2015. Intrusion Detection System Based on Modified K-means and Multi-level Support Vector Machines. In Soft Computing in Data Science, Michael W. Berry, Azlinah Mohamed, and Bee Wah Yap (Eds.). Springer Singapore, Singapore, 265--274.

4. Shadi Aljawarneh. 2011. A web engineering security methodology for e-learning systems. Network Security 2011, 3 (2011), 12--15. https://doi.org/10.1016/S1353-4858(11)70026-5

5. Shadi Aljawarneh, Muneer Bani Yassein, and We'am Adel Talafha. 2017. A resource-efficient encryption algorithm for multimedia big data. Multimedia Tools and Applications 76, 21 (01 Nov 2017), 22703--22724. https://doi.org/10.1007/s11042-016-4333-y

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