An Efficient Anomaly Intrusion Detection Method With Feature Selection and Evolutionary Neural Network

Sarvari, Samira; Sani, Nor Fazlida Mohd; Hanapi, Zurina Mohd; Abdullah, Mohd Taufik

doi:10.1109/access.2020.2986217

Cited by 59 publications

(23 citation statements)

References 20 publications

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“…Pandey et al [133] introduced binary binomial cuckoo search algorithm to select the best features and applied to fourteen benchmark datasets of UCI [134]. Although lots of applications of machine learning have been solved by developing different versions of CS algorithm [135]- [137].…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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“…Also, this process provides the model with the removal of the redundant information and improvement in the generalization [41]. Many techniques are used for feature selection, such as Gain Ratio (GR), Symmetrical uncertainty, Chi-Square analysis, Information Gain (IG), and Practical Swarm Optimization (PSO) [42], [43].…”

Section: Effectiveness Of Dimensionality Reduction For Feature Selectionmentioning

confidence: 99%

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih

Abdulazeez

2021

JSCDM

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Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.

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“…An enhancement of Cuckoo Search Algorithm (CSA), named Mutation Cuckoo Fuzzy (MCF) was proposed by [94] for feature selection method and multiverse optimization ANN for classification at anomaly-based IDS. For feature selection phase, MCF that integrates mutation operator with cuckoo search and Fuzzy C Means (FCM) clustering was utilized.…”

Section: ) Filtermentioning

confidence: 99%

A Review on Feature Selection and Ensemble Techniques for Intrusion Detection System

Torabi¹,

Udzir²,

Abdullah³

et al. 2021

IJACSA

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Intrusion detection has drawn considerable interest as researchers endeavor to produce efficient models that offer high detection accuracy. Nevertheless, the challenge remains in developing reliable and efficient Intrusion Detection System (IDS) that is capable of handling large amounts of data, with trends evolving in real-time circumstances. The design of such a system relies on the detection methods used, particularly the feature selection techniques and machine learning algorithms used. Thus motivated, this paper presents a review on feature selection and ensemble techniques used in anomaly-based IDS research. Dimensionality reduction methods are reviewed, followed by the categorization of feature selection techniques to illustrate their effectiveness on training phase and detection. Selection of the most relevant features in data has been proven to increase the efficiency of detection in terms of accuracy and computational efficiency, hence its important role in the design of an anomaly-based IDS. We then analyze and discuss a variety of IDS-based machine learning techniques with various detection models (single classifier-based or ensemble-based), to illustrate their significance and success in the intrusion detection area. Besides supervised and unsupervised learning methods in machine learning, ensemble methods combine several base models to produce one optimal predictive model and improve accuracy performance of IDS. The review consequently focuses on ensemble techniques employed in anomaly-based IDS models and illustrates how their use improves the performance of the anomaly-based IDS models. Finally, the paper laments on open issues in the area and offers research trends to be considered by researchers in designing efficient anomaly-based IDSs.

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An Efficient Anomaly Intrusion Detection Method With Feature Selection and Evolutionary Neural Network

Cited by 59 publications

References 20 publications

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

A Review on Feature Selection and Ensemble Techniques for Intrusion Detection System

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