Open Access

ARTICLE

Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

by N. Venkateswaran^1,*, K. Umadevi²

1 Information and Communication Engineering, Anna University, Chennai, 600025, India
2 Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengodu, 637205, India

* Corresponding Author: N. Venkateswaran. Email:

Computer Systems Science and Engineering 2022, 42(1), 1-14. https://doi.org/10.32604/csse.2022.021217

Received 27 June 2021; Accepted 30 July 2021; Issue published 02 December 2021

Abstract

Huge data over the cloud computing and big data are processed over the network. The data may be stored, send, altered and communicated over the network between the source and destination. Once data send by source to destination, before reaching the destination data may be attacked by any intruders over the network. The network has numerous routers and devices to connect to internet. Intruders may attack any were in the network and breaks the original data, secrets. Detection of attack in the network became interesting task for many researchers. There are many intrusion detection feature selection algorithm has been suggested which lags on performance and accuracy. In our article we propose new IDS feature selection algorithm with higher accuracy and performance in detecting the intruders. The combination of wrapper filtering method using Pearson correlation with recursion function is used to eliminate the unwanted features. This feature extraction process clearly extracts the attacked data. Then the deep neural network is used for detecting intruders attack over the data in the network. This hybrid machine learning algorithm in feature extraction process helps to find attacked information using recursive function. Performance of proposed method is compared with existing solution. The traditional feature selection in IDS such as differential equation (DE), Gain ratio (GR), symmetrical uncertainty (SU) and artificial bee colony (ABC) has less accuracy than proposed PCRFE. The experimented results are shown that our proposed PCRFE-CDNN gives 99% of accuracy in IDS feature selection process and 98% in sensitivity.

---

Keywords

---