A multi-measure feature selection algorithm for efficacious intrusion detection

Herrera-Semenets, Vitali; Bustio-Martínez, Lázaro; Hernández-León, Raudel; Berg, Jan van den

doi:10.1016/j.knosys.2021.107264

Cited by 26 publications

(4 citation statements)

References 35 publications

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“…Furthermore, according to the study's findings, the F-measure and Receiver operating characteristic (ROC) curve scores suggest that binary classification using the Bernoulli Naive Bayes method works effectively. Herrera-Semenets et al [65] feature selection algorithm, called Multi Measure Feature Selection Algorithm (MMFSA), combines three measures to estimate different qualitative information in the features. The algorithm was evaluated and compared with other feature selection algorithms on a dataset, and it was found that MMFSA outperforms the other algorithms regarding classifier efficacy.…”

Section: Literature Reviewmentioning

confidence: 99%

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Bouke,

Abdullah,

Udzir

et al. 2024

JCIS

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The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized internet-based technologies and have led to an increase in information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS have recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve IDS model.

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Section: Literature Reviewmentioning

confidence: 99%

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Bouke,

Abdullah,

Udzir

et al. 2024

JCIS

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show abstract

“…Intrusion detection involves the identification of unauthorized activities [29]. It entails the collection and analysis of network behavior, security logs, audits, data, information available on other networks, and critical information from various points within a computer system.…”

Section: Data-level Improvementmentioning

confidence: 99%

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

Lu,

Cao,

Wang

2024

Applied Sciences

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To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.

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“…In recent years, many hybrid approaches combine with feature selection techniques and Stacking ensemble method to improve the performance of IDSs. Herrera-Semenets et al [26] proposed an IDS based on multi-measure feature selection algorithm. They combine Information Gain (IG), chi square and ReliefF algorithms to construct multi-measure feature selection algorithm.…”

Section: Hybrid Approachesmentioning

confidence: 99%

A Hybrid Intrusion Detection System Based on Feature Selection and Weighted Stacking Classifier

Zhao

Zou

et al. 2022

IEEE Access

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Cyber-attacks occur more frequently with the rapid growth in the Internet. Intrusion detection systems (IDS) have become an important part of protecting system security. There are still some challenges preventing IDS from further improving its classification performance. Firstly, the complexity of high-dimensional features challenges the speed and the performance of the classification for IDS. Secondly, the classification performance of traditional Stacking algorithm can be easily affected by the base classifiers. Tackling both challenges above, we propose a hybrid intrusion detection system based on a CFS-DE feature selection algorithm and a weighted Stacking classification algorithm. To limit the dimension of the features, we deployed the CFS-DE algorithm, which searches for the optimal feature subset. Afterwards, a weighted Stacking algorithm is proposed, which increases the weights of the base classifiers with good training results and drops those base classifiers with bad ones to improve the classification performance. As such, the model enhances the classification efficiency and yielding better accuracy. All experiments in this study were conducted on the NSL-KDD and CSE-CIC-IDS2018 data sets. The results based on KDDTest+ show that our proposed model has accuracy of 87.44%, precision of 89.09%, recall of 87.44% and F1-score of 88.25%. The results based on CSE-CIC-IDS2018 show that our proposed model has accuracy of 99.87%, precision of 99.88%, recall of 99.87% and F1-score of 99.88%. Compared with traditional machine learning models and models mentioned in other papers, out proposed CFS-DE-weighted-Stacking IDS has the best classification performance.

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A multi-measure feature selection algorithm for efficacious intrusion detection

Cited by 26 publications

References 35 publications

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

A Hybrid Intrusion Detection System Based on Feature Selection and Weighted Stacking Classifier

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