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

摘要

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.