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.
Highlights
Nowadays Computer networks, wireless networks are widely used by variety of applications which are prone to myriad of security threats and attacks
To address the issues in feature selection methods, this paper proposed a hybrid feature selection called Pearson correlation based Recursive feature elimination to select the relevant features that are close to the data which will increase the classification accuracy
The rest of the paper is organized as follows: Section 2 outlines the literature related to intrusion detection systems (IDS), Section 3 introduces the proposed feature selection and classification algorithm called Pearson Correlation based Recursive Feature Elimination (PCRFE)-Convolutional Deep Neural Network (CDNN)-IDS, Section 4 presents the experimented results and analysis of the comparative study and Section 5 concludes the paper with the future work
Summary
Nowadays Computer networks, wireless networks are widely used by variety of applications which are prone to myriad of security threats and attacks. DL has been applied to various fields such as language identification, image processing and pharmaceutical research [16,17,18] With this knowledge, the DL technique called Convolutional Deep Neural Network (CDNN) is applied on our work for classification. Our contribution of the paper is as follows, This work proposed a hybrid wrapper feature selection method called Pearson Correlation based Recursive Feature Elimination (PCRFE) to remove the redundant and irrelevant features from the dataset. This evaluate the correlation between the features and generate the subset of relevant features using the Recursive Feature elimination technique. The rest of the paper is organized as follows: Section 2 outlines the literature related to IDS, Section 3 introduces the proposed feature selection and classification algorithm called PCRFE-CDNN-IDS, Section 4 presents the experimented results and analysis of the comparative study and Section 5 concludes the paper with the future work
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