Linear Correlation-Based Feature Selection for Network Intrusion Detection Model

Eid, Heba F.; Hassanien, Aboul Ella; Kim, Tai-hoon; Banerjee, Soumya

doi:10.1007/978-3-642-40597-6_21

Cited by 58 publications

(33 citation statements)

References 11 publications

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“…NSL-KDD dataset, an improved version of KDD CUP '99 widely deployed in the literature [26,29,42] for intrusion detection, was used to validate our proposed algorithm. NSL-KDD is a labelled benchmark dataset from KDD CUP '99 to improve its flaws.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Osanaiye

Cai

Choo

et al. 2016

J Wireless Com Network

263

136

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Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud's bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

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Section: Benchmark Datasetsmentioning

confidence: 99%

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Osanaiye

Cai

Choo

et al. 2016

J Wireless Com Network

263

136

View full text Add to dashboard Cite

show abstract

“…A zero-correlation value demonstrates that the feature and the target variable are independent. 48,49 Compared to other correlation methods such as Spearman and Kendall, the Pearson correlation has proved to be computationally efficient. 49 LC and RReliefF belong to the univariate feature filter techniques since they assess each attribute separately.…”

Section: Filter Feature Selectionmentioning

confidence: 99%

“…48,49 Compared to other correlation methods such as Spearman and Kendall, the Pearson correlation has proved to be computationally efficient. 49 LC and RReliefF belong to the univariate feature filter techniques since they assess each attribute separately. The output of a univariate filter feature selection is the ranking of features according to relevance.…”

Section: Filter Feature Selectionmentioning

confidence: 99%

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Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Hosni

Idri

Abran

2018

J Software Evolu Process

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Researchers have developed and evaluated many techniques to deliver accurate estimates of the effort required to complete a new software program. Among these, analogy has emerged as a very promising technique, in particular the fuzzy analogy estimation technique that uses the fuzzy logic concepts in order to deal with both categorical and numerical data. The aim of this paper is twofold: (1) evaluate the impact of 3 filters on the predictive ability of single and ensemble fuzzy analogy techniques and (2) assess whether filters could be a source of diversity for fuzzy analogy ensembles. Moreover, it compares the filter single and ensemble fuzzy analogy techniques with fuzzy analogy ensembles built without using feature selection over 6 datasets. The overall results suggest that (1) more accurate estimates are generated when filters were used with single and ensemble fuzzy analogy techniques, (2) filter single fuzzy analogy techniques outperformed filter fuzzy analogy ensembles, and (3) fuzzy analogy ensembles without feature selection were more accurate than filter single and ensemble techniques. Therefore, though the use of feature selection techniques led single and ensemble fuzzy analogy to generate accurate estimations, they failed to be a source of diversity for fuzzy analogy ensembles. Hence, constructing fuzzy analogy homogenous ensembles that combine single fuzzy analogy techniques with different parameter configurations still generate better accuracy than filter fuzzy analogy ensembles. However, further empirical evaluations of filter/wrappers fuzzy analogy ensembles are required in order to confirm or refute these findings.

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“…Kim K. J. et al [8] have presented many optimization techniques that can improve the performance of a neural network for classification tasks. To improve the processing time and detection performance of the proposed method relevant features are selected using a Correlation-based Feature Selection method [9], [10]. The proposed method was evaluated on two datasets namely NSL-KDD [11] and UNSW-NB15 [12], [13].…”

Section: Introductionmentioning

confidence: 99%

DoS Detection Method based on Artificial Neural Networks

Idhammad¹,

Afdel²,

Belouch³

2017

ijacsa

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Abstract-DoS attack tools have become increasingly sophisticated challenging the existing detection systems to continually improve their performances. In this paper we present a victimend DoS detection method based on Artificial Neural Networks (ANN). In the proposed method a Feed-forward Neural Network (FNN) is optimized to accurately detect DoS attack with minimum resources usage. The proposed method consists of the following three major steps: (1) Collection of the incoming network traffic, (2) selection of relevant features for DoS detection using an unsupervised Correlation-based Feature Selection (CFS) method, (3) classification of the incoming network traffic into DoS traffic or normal traffic. Various experiments were conducted to evaluate the performance of the proposed method using two public datasets namely UNSW-NB15 and NSL-KDD. The obtained results are satisfactory when compared to the state-of-the-art DoS detection methods.

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Linear Correlation-Based Feature Selection for Network Intrusion Detection Model

Cited by 58 publications

References 11 publications

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

DoS Detection Method based on Artificial Neural Networks

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