Ensemble-Based Filter Feature Selection Technique for Building Flow-Based IDS

Karna, Ishita; Madam, Aniket; Deokule, Chinmay; Adhao, Rahul; Pachghare, Vinod

doi:10.1109/access51619.2021.9563297

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…By comparing the swarm intelligence search algorithms such as GA [32] and CFS [28], the method in this paper achieves a higher accuracy and higher false alarm rate than these two methods. In comparison with the ensemble feature selection strategies such as SCM3 [21], CPM [39], and EFW [40], these three methods achieve 99.88%, 99.16%, and 99.25% accuracy, respectively, while the accuracy of this paper reaches 99.92%, relative to the F1 value of 99.17% achieved by the CPM method, which achieves an F1 value of 99.92%.…”

Section: Discussionmentioning

confidence: 73%

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An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection

Zhang

2022

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The mass of redundant and irrelevant data in network traffic brings serious challenges to intrusion detection, and feature selection can effectively remove meaningless information from the data. Most current filtered and embedded feature selection methods use a fixed threshold or ratio to determine the number of features in a subset, which requires a priori knowledge. In contrast, wrapped feature selection methods are computationally complex and time-consuming; meanwhile, individual feature selection methods have a bias in evaluating features. This work designs an ensemble-based automatic feature selection method called EAFS. Firstly, we calculate the feature importance or ranks based on individual methods, then add features to subsets sequentially by importance and evaluate subset performance comprehensively by designing an NSOM to obtain the subset with the largest NSOM value. When searching for a subset, the subset with higher accuracy is retained to lower the computational complexity by calculating the accuracy when the full set of features is used. Finally, the obtained subsets are ensembled, and by comparing the experimental results on three large-scale public datasets, the method described in this study can help in the classification, and also compared with other methods, we discover that our method outperforms other recent methods in terms of performance.

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Section: Discussionmentioning

confidence: 73%

“…On the UNSW-NB15 dataset, the accuracy was 98.8%. Karna et al [39] used mutual information, Chi-square, and Pearson correlation coefficients to combine a more stable subset of features. It achieved 99.16% accuracy on the CIC-IDS2017 dataset using 25 features.…”

Section: Related Workmentioning

confidence: 99%

An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection

Zhang

2022

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“…The proposed ensemble classifier technique of [ 47 ] achieved 86.5% accuracy, while model presented in [ 66 ] achieved 99.86% accuracy. Other techniques that were proposed in [ 25 , 34 , 48 , 49 ], received, 97.72%, 99.89%, 99.95%, and 98.62%accuracy, while our proposed scheme is 99.98% accurate during the classification. The reported FPR using the CIC-IDS2017 dataset is 0.12 and 0.013, acquired by techniques proposed in [ 25 , 34 ], which is further reduced to 0.00012 by our proposed ensemble classifier.…”

Section: Discussionmentioning

confidence: 81%

“…They tested the proposed scheme using the MAWILab and CIC-IDS2017 dataset and the technique achieved 86.50% accuracy. Furthermore, Karna et al presented a filter-based selection technique and used an ensemble classifier that was composed of DT, RT, and ET algorithms [ 48 ]. This technique was tested for NSL-KDD and CIC-IDS2017 and achieved 99.51% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Farooqi,

Akhtar,

Rahman

et al. 2023

Sensors

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In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

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Statistical feature selection based intrusion detection system for internet of things environment

Adhao¹,

Tayde²,

Pachghare³

2023

Computational Intelligence and Network Security

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Ensemble-Based Filter Feature Selection Technique for Building Flow-Based IDS

Cited by 5 publications

References 11 publications

An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection

An Effective Ensemble Automatic Feature Selection Method for Network Intrusion Detection

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Statistical feature selection based intrusion detection system for internet of things environment

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