Prepare for trouble and make it double! Supervised – Unsupervised stacking for anomaly-based intrusion detection

Zoppi, Tommaso; Ceccarelli, Andrea

doi:10.1016/j.jnca.2021.103106

Cited by 21 publications

(2 citation statements)

References 66 publications

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“…They used grid search algorithm to optimise the combination of secondary classifiers. In order to identify zero-day attacks, Tommaso Zoppi et al [24] proposed a Stacking algorithm, while building a two-layer hybrid Stacking algorithm. Supervised and unsupervised learning methods were both used as base classifiers in this algorithm.…”

Section: B Stacking Ensemble Methodsmentioning

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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Section: B Stacking Ensemble Methodsmentioning

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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“…Regarding robustness and accuracy, GWO achieves higher. If time is of higher concern, then the ES algorithm takes less time [101].…”

Section: Comparative Analysismentioning

confidence: 99%

Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection

et al. 2022

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Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have incentivized many researchers to propose different artificial intelligence-based techniques to prevent, detect, and respond to such advanced attacks. This has also created a new requirement for a comprehensive comparison of the existing schemes in several aspects ; after a thorough study we found that there currently exists no detailed comparative analysis of artificial intelligence-based techniques published in the last five years. Therefore, there is a need for this kind of work to be published, as there are many comparative analyses in other fields of cyber security that are available for readers to review.In this paper, we provide a comprehensive review of the latest and most recent literature, which introduces well-known machine learning and deep learning algorithms and the challenges they face in detecting zero-day attacks. Following these qualitative analyses, we present the comparative evaluation results regarding the highest accuracy, precision, recall, and F1 score compared to different datasets.

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Exploring the effect of training-time randomness on the performance of deep neural networks for intrusion detection

Catillo,

Pecchia,

Villano

2024

Soft Comput

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Prepare for trouble and make it double! Supervised – Unsupervised stacking for anomaly-based intrusion detection

Cited by 21 publications

References 66 publications

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

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

Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection

Exploring the effect of training-time randomness on the performance of deep neural networks for intrusion detection

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