I-AHSDT: intrusion detection using adaptive dynamic directive operative fractional lion clustering and hyperbolic secant-based decision tree classifier-Reference-Cited by-同舟云学术

I-AHSDT: intrusion detection using adaptive dynamic directive operative fractional lion clustering and hyperbolic secant-based decision tree classifier

Published:2018-08-23 Issue:6 Volume:30 Page:887-910
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Ganeshan R.¹,Paul Rodrigues S.²

Affiliation:

1. Department of Computer Science and Engineering, ST.Joseph College of Engineering, Sriperumbudur, India

2. Department of Computer Science and Engineering, Indira Gandhi College of Engineering and Technology for Women, Chengalpattu, India

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2018.1509379

Reference35 articles.

1. A k-mean clustering algorithm for mixed numeric and categorical data

2. Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

3. An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

4. Scalable Lookahead Regular Expression Detection System for Deep Packet Inspection

5. A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

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