Filter Versus Wrapper Feature Selection for Network Intrusion Detection System

Sakr, Mohamed; Tawfeeq, Medhat A.; El-Sisi, Ashraf B.

doi:10.1109/icicis46948.2019.9014797

Cited by 8 publications

(12 citation statements)

References 13 publications

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“…The proposed PCRFE-CDNN-IDS system is compared with the existing approaches to analyze the performance using the performance metrics such as Accuracy, False positive rate (FPR), False negative rate (FNR), Sensitivity/True positive rate (TPR), Specificity/True negative rate (TNR) and recall/Attack Detection rate (ADR) [3]. The evaluation metrics equations are represented as The proposd work is evaluated with the total number of features and the selected fatures using proposed PCRFE feature selection.…”

Section: Evaluation Using Performance Metricsmentioning

confidence: 99%

“…The proposed work feature selection performance is compared with the existing FS on IDS such as, Discrete differential equation [4], Gain ratio [5], symmetrical uncertainty [6] and ABC [3]. The experimented results are shown in Tab.…”

Section: Evaluation Using Performance Metricsmentioning

confidence: 99%

“…The security challenges that have to be solved originate from the open nature, the flexibility and the mobility of the wireless communication medium [1,2]. In an effort to secure these networks, various preventive and protective mechanisms such as intrusion detection systems (IDS) were developed [3]. Primarily, IDS can be classified as: host based intrusion detection systems (HIDS) and network based intrusion detection systems (NIDS) [4].…”

Section: Introductionmentioning

confidence: 99%

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Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Venkateswaran¹,

Umadevi²

2022

Computer Systems Science and Engineering

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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.

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Section: Evaluation Using Performance Metricsmentioning

confidence: 99%

Section: Evaluation Using Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Venkateswaran¹,

Umadevi²

2022

Computer Systems Science and Engineering

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“…Wrapper models comprise of various number of techniques [6]projected a set of sequential search strategies to discover subset of salient features. In [7], features have been included to Neural Network (NN) on the basis of SFS while learning operation is carried out.…”

Section: Introductionmentioning

confidence: 99%

“…[15], evaluated the saliency of features with the help of NN training approach where single feature is deployed in input layer simultaneously. In order to determine the saliency of a feature, two diverse weights analysis-based heuristic methods have been applied [7]. Therefore, [14]introduced a complete feature set for NN training scheme which is employed for all feature that is removed for particular time using a cross-check of NN operation.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Feature Selection Model using Fast Convergence Ant Colony Optimization Algorithm

Suresh¹

2020

IJETER

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Nowadays, several disciplines have to deal with big datasets that additionally comprise a high number of features. It gained more interest in several application domains like bioinformatics, medicine, marketing, or financial businesses, owing to massive collection of raw data that are stored. Feature selection (FS) is a process of choosing a minimal set of features from the actual set of features for optimal reduction in the feature space based on particular validation parameter. Since the dimensionality of a domain gets increased, the feature count N will also get increased. The process of identifying the optimal feature set is generally difficult and several issues relevant to FS have been shown to be a non-polynomial (NP) hard problem. This paper proposes a hybridization of ant colony optimization (ACO) with genetic algorithm (GA) for FS process. Since the ACO algorithm suffers from the drawback of slower convergence and improves the search space exploration process, GA is incorporated into the ACO algorithm. The application of GA in ACO algorithm for FS helps to fasten the convergence rate as well as improves the exploration capability. To assess the effective performance of the projected model, three different benchmark dataset namely chronic kidney disease (CKD), hepatitis and market dataset are utilized. The experimental results show the superior performance of the proposed model over the compared methods. .

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Solving Feature Selection Problems Built on Population‐based Metaheuristic Algorithms

Sassi

2022

Optimization and Machine Learning

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Filter Versus Wrapper Feature Selection for Network Intrusion Detection System

Cited by 8 publications

References 13 publications

Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

An Ensemble Feature Selection Model using Fast Convergence Ant Colony Optimization Algorithm

Solving Feature Selection Problems Built on Population‐based Metaheuristic Algorithms

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