An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (2024)

Table of Contents
Анотація Джерела фінансування Список літератури Публікації, які цитують цю публікацію FAQs References

An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (1) https://doi.org/10.3390/s22197436 · An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (2)

Видання: Sensors, 2022, №19, с.7436

Видавець: MDPI AG

Автори: Lifeng Lei, Liang Kou, Xianghao Zhan, Jilin Zhang, Yongjian Ren

Анотація

With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods.

Джерела фінансування

  1. Key Technology Research and Development Program of the Zhejiang Province
  2. National Natural Science Foundation of China

Список літератури

  1. McKeown, OpenFlow: Enabling innovation in campus networks, ACM SIGCOMM Comput. Commun. Rev., № 38, с. 69
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (3) https://doi.org/10.1145/1355734.1355746
  2. Ali, A Survey of Securing Networks Using Software Defined Networking, IEEE Trans. Reliab., № 64, с. 1086
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (4) https://doi.org/10.1109/TR.2015.2421391
  3. Rawat, Software defined networking architecture, security and energy efficiency: A survey, IEEE Commun. Surv. Tutor., № 19, с. 325
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (5) https://doi.org/10.1109/COMST.2016.2618874
  4. Bian, S., Zhang, P., and Yan, Z. (2016, January 18–20). A survey on software-defined networking security. Proceedings of the 9th EAI International Conference on Mobile Multimedia Communications, Xi’an, China.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (6) https://doi.org/10.4108/eai.18-6-2016.2264176
  5. Xu, Research Development of Abnormal Traffic Detection in Software Defined Networking, J. Softw., № 31, с. 25
  6. Scott-Hayward, S., O’Callaghan, G., and Sezer, S. (2013, January 11–13). SDN security: A survey. Proceedings of the 2013 IEEE SDN for Future Networks and Services (SDN4FNS), Trento, Italy.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (7) https://doi.org/10.1109/SDN4FNS.2013.6702553
  7. Hinton, A fast learning algorithm for deep belief nets, Neural Comput., № 18, с. 1527
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (8) https://doi.org/10.1162/neco.2006.18.7.1527
  8. Jackson, K., DuBois, D., and Stallings, C. (1991, January 1–4). An expert system application for network intrusion detection. Proceedings of the National Computer Security Conference, Washington, DC, USA.
  9. Javitz, H.S., and Valdes, A. (1991, January 20–22). The SRI IDES statistical anomaly detector. Proceedings of the 1991 IEEE Computer Society Symposium on Research in Security and Privacy, Oakland, CA, USA.
  10. Sekar, R., Gupta, A., Frullo, J., Shanbhag, T., Tiwari, A., Yang, H., and Zhou, S. (2002, January 18–22). Specification-based anomaly detection: A new approach for detecting network intrusions. Proceedings of the CCS02: ACM Conference on Computer and Communications Security, Washington, DC, USA.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (9) https://doi.org/10.1145/586143.586146
  11. Bauer, D.S., and Koblentz, M.E. NIDX—An expert system for real-time network intrusion detection. In Proceedings of the 1988 Computer Networking Symposium, Washington, DC, USA, 11–13 April 1988.
  12. Sinclair, C., Pierce, L., and Matzner, S. (1999, January 6–10). An application of machine learning to network intrusion detection. Proceedings of the 15th Annual Computer Security Applications Conference (ACSAC’99), Phoenix, AZ, USA.
  13. Goldstein, M., and Dengel, A. (2012, January 24–27). Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. Proceedings of the KI-2012: Poster and Demo Track, Saarbrücken, Germany.
  14. Wang, Statistical traffic anomaly detection in time-varying communication networks, IEEE Trans. Control Netw. Syst., № 2, с. 100
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (10) https://doi.org/10.1109/TCNS.2014.2378631
  15. Thottan, Anomaly detection in IP networks, IEEE Trans. Signal Process., № 51, с. 2191
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (11) https://doi.org/10.1109/TSP.2003.814797
  16. Buschkes, R., Kesdogan, D., and Reichl, P. (1998, January 7–11). How to increase security in mobile networks by anomaly detection. Proceedings of the 14th Annual Computer Security Applications Conference (Cat. No. 98EX217), Phoenix, AZ, USA.
  17. Sarasamma, Hierarchical Kohonenen Net for anomaly detection in network security, IEEE Trans. Syst. Man Cybern. Part B Cybern., № 35, с. 302
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (12) https://doi.org/10.1109/TSMCB.2005.843274
  18. Dimitriadis, Anomaly detection in network traffic based on statistical inference and α-stable modeling, IEEE Trans. Dependable Secur. Comput., № 8, с. 494
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (13) https://doi.org/10.1109/TDSC.2011.14
  19. Sui, S., Li, L., and Manikopoulo, C.N. (2006, January 23–25). Flow-based Statistical Aggregation Schemes for Network Anomaly Detection. Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, FL, USA.
  20. Nevat, Anomaly detection and attribution in networks with temporally correlated traffic, IEEE/ACM Trans. Netw., № 26, с. 131
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (14) https://doi.org/10.1109/TNET.2017.2765719
  21. Qu, D., Vetter, B.M., Wang, F., Narayan, R., Wu, S.F., Jou, Y.F., Gong, F., and Sargor, C. (1998, January 13–16). Statistical anomaly detection for link-state routing protocols. Proceedings of the Sixth International Conference on Network Protocols (Cat. No. 98TB100256), Austin, TX, USA.
  22. Soule, A., Salamatian, K., and Taft, N. (2005, January 19–21). Combining filtering and statistical methods for anomaly detection. Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement, Berkeley, CA, USA.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (15) https://doi.org/10.1145/1330107.1330147
  23. Li, Z., Zhao, Y., Botta, N., Ionescu, C., and Hu, X. (2020). COPOD: Copula-based outlier detection. arXiv.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (16) https://doi.org/10.1109/ICDM50108.2020.00135
  24. Maimo, A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks, IEEE Access, № 6, с. 7700
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (17) https://doi.org/10.1109/ACCESS.2018.2803446
  25. Van, N.T., Thinh, T.N., and Sach, L.T. (2017, January 21–23). An anomaly-based network intrusion detection system using Deep learning. Proceedings of the 2017 International Conference on System Science and Engineering (ICSSE), Ho Chi Minh City, Vietnam.
  26. Garg, A hybrid deep learning-based model for anomaly detection in cloud datacenter networks, IEEE Trans. Netw. Serv. Manag., № 16, с. 924
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (18) https://doi.org/10.1109/TNSM.2019.2927886
  27. Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., and Ghogho, M. (2016, January 26–29). Deep learning approach for Network Intrusion Detection in Software Defined Networking. Proceedings of the 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), Fez, Morocco.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (19) https://doi.org/10.1109/WINCOM.2016.7777224
  28. Garg, Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in SDN: A social multimedia perspective, IEEE Trans. Multimed., № 21, с. 566
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (20) https://doi.org/10.1109/TMM.2019.2893549
  29. Yang, L., Song, Y., Gao, S., Xiao, B., and Hu, A. (2020, January 7–11). Griffin: An Ensemble of AutoEncoders for Anomaly Traffic Detection in SDN. Proceedings of the GLOBECOM 2020—2020 IEEE Global Communications Conference, Taipei, Taiwan.
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (21) https://doi.org/10.1109/GLOBECOM42002.2020.9322187
  30. Ujjan, Towards sFlow and adaptive polling sampling for deep learning based DDoS detection in SDN, Future Gener. Comput. Syst., № 111, с. 763
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (22) https://doi.org/10.1016/j.future.2019.10.015
  31. Elsayed, InSDN: A novel SDN intrusion dataset, IEEE Access, № 8, с. 165263
    An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (23) https://doi.org/10.1109/ACCESS.2020.3022633

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An Anomaly Detection Algorithm Based on Ensemble Learning for 5G Environment (2024)

FAQs

What is anomaly detection in 5G? ›

Network Anomaly Detection (NAD) in 5G is a way to observe the network constantly to detect any unusual behavior. However, it is not that straightforward and rather a complex process due to huge, continuous, and stochastic network traffic patterns.

Show Me More ›
What is ensemble method for anomaly detection? ›

Ensemble Methods encompass a range of algorithms designed for anomaly detection, that leverage the power of multiple models to enhance the overall system's predictive performance. Generally, ensemble methods use multiple detectors, each detecting anomalies in its own unique manner.

Read More ›
Which algorithm is used for anomaly detection? ›

Isolation Forest

Isolation forest is an unsupervised anomaly detection algorithm that uses a random forest algorithm, or decision trees, under the hood to detect outliers in the data set. The algorithm tries to split or divide the data points such that each observation gets isolated from the others.

Learn More ›
Which algorithm works based on ensemble learning? ›

Random Forest. Random Forest is another ensemble machine learning algorithm that follows the bagging technique. It is an extension of the bagging estimator algorithm. The base estimators in random forest are decision trees.

Keep Reading ›
What are the three types of anomaly detection? ›

Anomaly detection techniques can be split into three main types - statistical methods, machine learning methods, and deep learning methods. Each one has its own best uses and strengths.

Explore More ›
How do you do anomaly detection? ›

Statistical tests can be used by data scientists to detect data anomalies by comparing the observed data with the expected distribution or pattern. For example, the Grubbs test can be used to identify outliers in a data set by comparing each data point to the mean and standard deviation of the data.

Learn More Now ›
Which technique is used for anomaly detection? ›

Outliers or anomaly detection can be detected using the Box-Whisker method or by DBSCAN. 2. Euclidean distance method is used with the items not correlated.

Discover More Details ›
What is the best classifier for anomaly detection? ›

Here are five popular techniques for anomaly detection that data scientists might employ:
  1. K-Nearest Neighbors (KNN) It is a well-known non-parametric instance-based approach for finding anomalies. ...
  2. Gaussian Mixture Model (GMM) ...
  3. Support Vector Machine (SVM)

View More ›
What are the three anomaly detection methods? ›

Depending on the type of data gathering anomalies, one might take into account the three anomaly detection methods and algorithms:
  • Unsupervised Clustering. ...
  • Supervised Classification. ...
  • Semi-supervised Detection.

Continue Reading ›
What are two of the more popular algorithms for anomaly detection? ›

Anomaly Detection Algorithms

Isolation Forest is a popular example that creates decision trees from a dataset by randomly selecting characteristics to detect similarities and isolate outliers. Cluster-based algorithms: these methods assign data points to clusters based on detected similarities.

Discover More ›

What is the most popular anomaly detection? ›

Anomaly Detection Methods And When to Use Each One

For univariate outlier detection, the most popular methods are: Z-score (standard score): the z-score measures how many standard deviations a data point is away from the mean. Generally, instances with a z-score over 3 are chosen as outliers.

Know More ›
What is an example of an ensemble algorithm? ›

Two very famous examples of ensemble methods are gradient-boosted trees and random forests. More generally, ensemble models can be applied to any base learner beyond trees, in averaging methods such as Bagging methods, model stacking, or Voting, or in boosting, as AdaBoost.

Learn More Now ›
Which of the following algorithms is an example of the ensemble learning algorithm? ›

Some examples of popular ensemble learning algorithms are a weighted average, stacked generalization (stacking), and bootstrap aggregation (bagging); see also classification in Fig.

Explore More ›
What is the technique for ensemble learning? ›

  • Simple Ensemble Learning methods. Voting and Averaging Based Ensemble methods are the very simple and easiest forms of ensemble learning. ...
  • Max Voting Classifier. Max voting is very much the same as average, the only difference is it is used for classification problems. ...
  • Weighted Averaging.

Show Me More ›
An Anomaly Detection Algorithm Based on ...ProQuesthttps://search.proquest.com ›

This applies to control plane service requests made via the northbound interface provided by the SDN regulator [4]. One or more SDN controllers comprise the con...
Abstract: With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated ...
No information is available for this page.

Explore More ›
What does an anomaly detector do? ›

Anomaly detection identifies suspicious activity that falls outside of your established normal patterns of behavior. A solution protects your system in real-time from instances that could result in significant financial losses, data breaches, and other harmful events.

Know More ›
What is the function of anomaly detection? ›

Anomaly detection is the process of analyzing company data to find data points that don't align with a company's standard data pattern. Companies use anomalous activity detection to define system baselines, identify deviations from that baseline, and investigate inconsistent data.

View Details ›
What is anomaly detection in signal processing? ›

The process of finding abnormal data is called anomaly detection. Regarding the time series data, there are three main types of anomalies, which are abnormal time points, time intervals, and time series [22]. Regarding the signals used in this study, several factors affected the signals and generated abnormal data.

See More ›
How does network anomaly detection work? ›

An effective approach to anomaly detection involves data collection, preprocessing, model training, evaluation, deployment, and continuous monitoring. Anomaly detection systems require regular updates and retraining to adapt to evolving network patterns and emerging threats.

Find Out More ›

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