An intrusion detection algorithm for sensor network based on normalized cut spectral clustering

Autoři: Gaoming Yang aff001;  Xu Yu aff002;  Lingwei Xu aff002;  Yu Xin aff003;  Xianjin Fang aff001
Působiště autorů: School of Computer Science & Engineering, Anhui University of Science & Technology, Huainan, Anhui, China aff001;  Department of Information Science & Technology, Qingdao University of Science & Technology, Qingdao, Shandong, China aff002;  Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0221920


Sensor network intrusion detection has attracted extensive attention. However, previous intrusion detection methods face the highly imbalanced attack class distribution problem, and they may not achieve a satisfactory performance. To solve this problem, we propose a new intrusion detection algorithm based on normalized cut spectral clustering for sensor network in this paper. The main aim is to reduce the imbalance degree among classes in an intrusion detection system. First, we design a normalized cut spectral clustering to reduce the imbalance degree between every two classes in the intrusion detection data set. Second, we train a network intrusion detection classifier on the new data set. Finally, we do extensive experiments and analyze the experimental results in detail. Simulation experiments show that our algorithm can reduce the imbalance degree among classes and reserves the distribution of the original data on the one hand, and improve effectively the detection performance on the other hand.

Klíčová slova:

Algorithms – Computer networks – Data acquisition – Machine learning algorithms – Support vector machines – Wireless sensor networks – Spectral clustering – Ranking algorithms


1. Umer MF, Sher M, Bi Y. A two-stage flow-based intrusion detection model for next-generation networks. PLoS ONE, 2018 Jan; 13(1): e0180945. doi: 10.1371/journal.pone.0180945

2. Han L, Zhou M, Jia W, Dalil Z, Xu X. Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model. Inform Sciences, 2019 Feb; 476: 491–504. doi: 10.1016/j.ins.2018.06.017

3. Bai F, Liu XY, Zhang YL, Lang DP. Research on game model of wireless sensor network intrusion detection. In: Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks, Beijing, China. 2019 Feb;p: 373-378.

4. Kumar N, Akash H, Prataap AR, Srinath G, Mala C. Intelligent intrusion detection system using decision tree classifier and bootstrap aggregation. In: 2018 8th International Symposium on Embedded Computing and System Design (ISED), Cochin, India. 2018 Dec.

5. Yu X, Jiang F, Du J, Gong D. A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains. Pattern Recognition, 2019 Oct; 94: 96–109. doi: 10.1016/j.patcog.2019.05.030

6. Povey D, Burget L, Agarwal M, Akyazi P, Kai F, Ghoshal A, et al. The subspace Gaussian mixture model—A structured model for speech recognition. Computer Speech and Language, 2011 Apr; 25(2):404–439. doi: 10.1016/j.csl.2010.06.003

7. Ahmad I, Basheri M, Iqbal MJ, Raheem A. Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access, 2018 May; 6: 33789-33795.

8. Gu B, Sheng VS. A robust regularization path algorithm for μ-support vector classification. IEEE Transactions on Neural Networks and Learning Systems, 2016 Feb; 28(5): 1241–1248. doi: 10.1109/TNNLS.2016.2527796 26929067

9. Yu X, Chu Y, Jiang F, Guo Y, Gong D. SVMs classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowledge-Based Systems, 2018 Feb; 141: 80–91. doi: 10.1016/j.knosys.2017.11.010

10. Sun X, Yan B, Zhang X, Rong C. An integrated intrusion detection model of cluster-based wireless sensor network. PLoS ONE, 2015 Oct; 10(10): e0139513. doi: 10.1371/journal.pone.0139513 26447696

11. Ring M, Landes D, Hotho A. Detection of slow port scans in flow-based network traffic. PLoS ONE, 2018 Sep; 13(9): e0204507. doi: 10.1371/journal.pone.0204507

12. Aburomman AA, Reaz M B I. A novel SVM-kNN-PSO ensemble method for intrusion detection system. Applied Soft Computing, 2016 Jan, 38: 360–372. doi: 10.1016/j.asoc.2015.10.011

13. Li KL, Huang HK, Tian SF. Fuzzy multi-class support vector machine and application in intrusion detection. Chinese Journal of Computers, 2005,28(2): 274–280.

14. Yang J, Yu X, Xie ZQ. A novel virtual sample generation method based on Gaussian distribution. Knowledge-Based Systems, 2011 Aug; 24(6):740–748. doi: 10.1016/j.knosys.2010.12.010

15. Tayal A, Coleman TF, Li Y. RankRC: Large-scale nonlinear rare class ranking. IEEE Transactions on Knowledge and Data Engineering, 2015 Dec; 27(12): 3347–3359. doi: 10.1109/TKDE.2015.2453171

16. Shams EA, Rizaner A. A novel support vector machine based intrusion detection system for mobile ad hoc networks. Wireless Networks, 2018 Jul; 24(5): 1821–1829. doi: 10.1007/s11276-016-1439-0

17. Zhang B, Liu Z, Jia Y, Ren J, Zhao X. Network intrusion detection method based on PCA and Bayes algorithm. Security and Communication Networks, 2018 Nov; Article ID 1914980. doi: 10.1155/2018/1914980

18. Xu L, Gulliver TA. Performance analysis for M2M video transmission cooperative networks using transmit antenna selection. Multimed Tools Applications, 2017 Nov; 76(22): 23891–23902. doi: 10.1007/s11042-016-4138-z

19. Deng L, Li D, Yao X, Cox D, Wang H. Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Computing, 2018 Jan; p: 1–16.

20. Wu Z, Leahy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993 Nov; 15: 1101–1113. doi: 10.1109/34.244673

21. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000 Aug; 22(8): 888–905. doi: 10.1109/34.868688

22. Chang C, Lin C. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011 Apr; 2(3): 389–396. doi: 10.1145/1961189.1961199

23. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. Joural of Artificial Intelligence Research, 2002 Jun; 16: 321–357. doi: 10.1613/jair.953

24. The KDD99 Dataset. Retrieved January 26, 2008, from

Článek vyšel v časopise


2019 Číslo 10