Data Dimensionality Reduction (DDR) Scheme for Intrusion Detection System Using Ensemble and Standalone Classifiers

Bansal, Ashu; Kaur, Sanmukh

doi:10.1007/978-981-13-9939-8_39

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Ashu Bansal et al [11], has showed that the big data has increased malicious activities such as MITM, DDos and Spoofing. The data dimensionality reduction scheme was proposed to minimize the dimensionality of data to get better detection rate.…”

Section: Literature Surveymentioning

confidence: 99%

A Robust Detection of Cyber Incidents Utilizing Machine Learning Techniques

JOGI,

SAIPRIYA,

TEHZEEBA

et al. 2024

TURCOMAT

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A reliable Cyber Attack Detection Model (CADM) is a system that works as safeguard for the users of modern technological devices and assistant for the operators of networks. The research paper aims to develop a CADM for analyzing the network data patterns to classify cyber-attacks. CADM finds out attack wise detection accuracy using ensemble classification method. LASSO has been used to extract important features. It can work with large datasets, and it has more visualization capability. Gradient Boosting and Random Forest algorithms have been used for classification of network traffic data to build an ensemble method. Gradient Boosting algorithm trains weak learning models and select the best decision trees to deliver more improved prediction accuracy and Random Forest algorithm trains each tree in parallel manner. In this research work, Jive datasets such as NSL-KDD, KDD Cup 99, UNSWNB15, URL 2016 and CICIDS 2017 are also applied to check the efficiency of the proposed model.

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Section: Literature Surveymentioning

confidence: 99%

A Robust Detection of Cyber Incidents Utilizing Machine Learning Techniques

JOGI,

SAIPRIYA,

TEHZEEBA

et al. 2024

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…This paper proposes a Data Dimensionality Reduction Scheme for Intrusion Detection Systems using Ensemble and Standalone Classifiers. The proposed Dimensionality Reduction scheme incorporates one technique from each type of correlation measure, as Symmetric Uncertainty as Entropy based Filter, Chi Squared as Statistical Measure and Relief F as Instance based Measure [29]. Artificial Neural Network based systems are adopted by numerous researchers for training and testing their models [30], [31], [32].…”

Section: ░ 4 Literature Surveymentioning

confidence: 99%

Investigating the Impact of Feature Reduction Through Information Gain and Correlation on the Performance of Error Back Propagation Based IDS

Dubey¹

2021

IJEER

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Research in the field of IDS has been going on since long time; however, there exists a number of ways to further improve the efficiency of IDS. This paper investigates the performance of Intrusion detection system using feature reduction and EBPA. The first step involves the reduction in number of features, based on the combination of information gain and correlation. In the next step, error back propagation algorithm (EBPA) is used to train the network and then analyze the performance. EBPA is commonly used due to its ease of use, high accuracy and efficiency. The proposed model is tested over the KDD Cup 99 and NSL-KDD datasets. Results show that the proposed IDS model with reduced feature set outperforms the other models, both in terms of performance metrics and processing time.

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A Framework for Classifying Web Attacks While Respecting ML Requirements

Rabah¹,

Tekeya²

2020

Advanced Information Networking and Applications

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Data Dimensionality Reduction (DDR) Scheme for Intrusion Detection System Using Ensemble and Standalone Classifiers

Cited by 6 publications

References 28 publications

A Robust Detection of Cyber Incidents Utilizing Machine Learning Techniques

A Robust Detection of Cyber Incidents Utilizing Machine Learning Techniques

Investigating the Impact of Feature Reduction Through Information Gain and Correlation on the Performance of Error Back Propagation Based IDS

A Framework for Classifying Web Attacks While Respecting ML Requirements

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