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

This paper proposes an effective intrusion detection method, named I-AHSDT, which is the combination of the Adaptive Dynamic Directive Operative Fractional Lion clustering (ADDOFL) and Hyperbolic Secant-based Decision Tree classifier (HSDT). The proposed HSDT classifier is based on the inverse hyper...

Full description

Saved in:
Bibliographic Details
Published in:Journal of experimental & theoretical artificial intelligence 2018-11, Vol.30 (6), p.887-910
Main Authors: Ganeshan, R., Paul Rodrigues, S.
Format: Article
Language:English
Subjects:
Classification
Classifiers
Clustering
Cybersecurity
Decision trees
directive operational algorithm
Experimentation
holoentropy decision tree
Hyperbolic functions
hyperbolic secant function
Intrusion detection
Intrusion detection systems
lion optimisation algorithm
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an effective intrusion detection method, named I-AHSDT, which is the combination of the Adaptive Dynamic Directive Operative Fractional Lion clustering (ADDOFL) and Hyperbolic Secant-based Decision Tree classifier (HSDT). The proposed HSDT classifier is based on the inverse hyperbolic secant function and it performs the two level classification to detect the intrusion, which offers robust classification performance. The experimentation is performed using the KDD Cup 1999 data, and the HCR Lab data set, and the experimental results prove that the proposed method outperforms the existing system in terms of the accuracy, which is 0.95.
ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2018.1509379