WITHDRAWN: An improved harmony search based extreme learning machine for intrusion detection system

Abstract

Intrusion Detection System (IDS) is one of the best ways to combat several cybercrimes, both at the edge of the network and inside the segments of the internal network. This article proposes a hybrid learning approach namely Improved Harmony Search Extreme Learning Machine based IDS (IHSELMIDS) to classify NSL KDD dataset. This dataset is a polished version of its predecessor i.e., Knowledge Discovery in Databases (KDD). Since 1999, many researchers have relied on the dataset of KDD for the evaluation of anomaly detection system. Thus, it is popularly known as KDD”99 dataset. Improved Harmony Search has been used to boost the weights of input and latent biases for a more robust and stable Extreme Learning Machine (ELM). In addition, the generalized inverse Moore – Penrose is used to systematically evaluate the weights of output. Additionally, to address the curse of high dimensionality in the dataset, correlation-based feature selection with greedy hill climbing is proposed which reduces time complexity while increases computational efficiency. A series of performance evaluation measures such as training and testing accuracy, True Positive Rate (TPR), True Negative Rate (TNR), G-mean, F-score, False Alarm Rate (FAR), Receiver Operating Characteristic curve (ROC) and Confusion Matrix is into consideration to contrast and examine the efficiency, flexibility and reliability of the proposed model. The experimental result showed that IHSELMIDS outperforms all the benchmark models considered in this study.