<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a typical boosting algorithm, transforms weak learners or predictors to strong predictors in order to solve problems of classification. With remarkable usability and effectiveness, AdaBoost algorithm has been widely used in many fields, such as face recognition, speech enhancement, natural language processing, and network intrusion detection. In the large-scale enterprise network environment, more and more companies have begun to build trustworthy networks to effectively defend against hacker attacks. However, since trustworthy networks use trusted flags to verify the legitimacy of network requests, it cannot effectively identify abnormal behaviors in network data packets. This paper applies Adaboost algorithm in trustworthy network for anomaly intrusion detection to improve the defense capability against network attacks. This method uses a simple decision tree as the base weak learner, and uses AdaBoost algorithm to combine multiple weak learners into a strong learner by re-weighting the samples. This paper uses the real data of trustworthy network for experimental verification. The experimental results show that the average precision of network anomaly detection method based on AdaBoost algorithm is more than 0.999, indicating that it has a significant detection effect on abnormal network attacks and normal network access. Therefore, the proposed method can effectively improve the security of trustworthy networks.</p>
</abstract>

Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a typical boosting algorithm, transforms weak learners or predictors to strong predictors in order to solve problems of classification. With remarkable usability and effectiveness, AdaBoost algorithm has been widely used in many fields, such as face recognition, speech enhancement, natural language processing, and network intrusion detection. In the large-scale enterprise network environment, more and more companies have begun to build trustworthy networks to effectively defend against hacker attacks. However, since trustworthy networks use trusted flags to verify the legitimacy of network requests, it cannot effectively identify abnormal behaviors in network data packets. This paper applies Adaboost algorithm in trustworthy network for anomaly intrusion detection to improve the defense capability against network attacks. This method uses a simple decision tree as the base weak learner, and uses AdaBoost algorithm to combine multiple weak learners into a strong learner by re-weighting the samples. This paper uses the real data of trustworthy network for experimental verification. The experimental results show that the average precision of network anomaly detection method based on AdaBoost algorithm is more than 0.999, indicating that it has a significant detection effect on abnormal network attacks and normal network access. Therefore, the proposed method can effectively improve the security of trustworthy networks.

AdaBoost Algorithm in Trustworthy Network for Anomaly Intrusion Detection

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"AdaBoost Algorithm in Trustworthy Network for Anomaly Intrusion Detection"}

Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a...

AdaBoost Algorithm in Trustworthy Network for Anomaly Intrusion Detection

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 1819 - 1830

Eingereicht: 25. Apr. 2022

Akzeptiert: 27. Juni 2022

DOI: https://doi.org/10.2478/amns.2022.2.0171

Schlüsselwörter
trustworthy network, zero trust, Adaboost, anomaly intrusion detection, intrusion detection systems

© 2023 Wei Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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AdaBoost Algorithm in Trustworthy Network for Anomaly Intrusion Detection

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 1819 - 1830

Eingereicht: 25. Apr. 2022

Akzeptiert: 27. Juni 2022

DOI: https://doi.org/10.2478/amns.2022.2.0171

Schlüsselwörtertrustworthy network, zero trust, Adaboost, anomaly intrusion detection, intrusion detection systems

© 2023 Wei Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Schlüsselwörter
trustworthy network, zero trust, Adaboost, anomaly intrusion detection, intrusion detection systems