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An enhanced Grey Wolf Optimizer based Particle Swarm Optimizer for intrusion detection system in wireless sensor networks

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Abstract

The intrusion detection system is a method for detection against attacks, making it one of the essential defense layers. Researchers are trying to find new algorithms to inspect all inbound and outbound activities and identify suspicious patterns that may show an attempted system attack. The proposed technique for detecting intrusions uses the Grey Wolf Optimization (GWO) to solve feature selection problems and hybridizing it with Particle Swarm Optimization (PSO) to utilize the best value to update the information of each grey wolf position. This technique preserves the individual's best position information by the PSO algorithm, which prevents the GWO algorithm from falling into a local optimum. The NSL KDD dataset is used to verify the performance of the proposed technique. The classification is done using the k-means and SVM algorithms to measure the performance in terms of accuracy, detection rate, false alarm rate, number of features, and execution time. The results have shown that the proposed technique attained the necessary improvement of the GWO algorithm when using K-means or SVM algorithms.

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References

  1. Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal (on-line). Available: https://www.rfidjournal.com/that-internet-of-things-thing.

  2. Abualigah, L., Diabat, A., & Elaziz, M. A. (2021). Intelligent workflow scheduling for Big Data applications in IoT cloud computing environments. Cluster Computing, 24, 2957–2976. https://doi.org/10.1007/s10586-021-03291-7.

    Article  Google Scholar 

  3. Singh, A., Nagar, J., Sharma, S., & Kotiyal, V. (2021). A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Systems With Applications, 172, 114603.

    Article  Google Scholar 

  4. Almomani, I., & Alromi, A. (2020). Integrating software engineering processes in the development of efficient intrusion detection systems in wireless sensor networks. Sensors, 20(5), 1375.

    Article  Google Scholar 

  5. Ullo, S. L., & Sinha, G. R. (2020). Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20(11), 3113.

    Article  Google Scholar 

  6. Fahmy, H. M. A. (2020). Wireless sensor networks: Energy harvesting and management for research and industry. Springer.

    Book  Google Scholar 

  7. Huo, G., & Wang, X. (2008). DIDS: A dynamic model of intrusion detection system in wireless sensor networks. In 2008 International Conference on Information and Automation (pp. 374–378). IEEE.

  8. Bace, R., & Mell, P. (2001). NIST special publication on intrusion detection systems. Booz-allen and Hamilton Inc MCLEAN VA.

  9. Lu, M., & Reeves, J. (2014). Types of cyber attacks. Trustworthy Cyber Infrastructure for the Power Grid, 18, 2017.

    Google Scholar 

  10. Liao, H. J., Lin, C. H. R., Lin, Y. C., & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16–24.

    Article  Google Scholar 

  11. Özgür, A., & Erdem, H. (2016). A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015. PeerJ Preprints, 4, e1954v1.

    Google Scholar 

  12. Abualigah, L., & Diabat, A. (2020). A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Computing and Applications, 1–24.

  13. Abualigah, L., & Diabat, A. (2021). Advances in sine cosine algorithm: A comprehensive survey. Artificial Intelligence Review, 1–42.

  14. Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2020). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609.

    Article  MathSciNet  Google Scholar 

  15. Singh, N., & Singh, S. B. (2017). Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance. Journal of Applied Mathematics2017.

  16. Singh, N. (2018). A modified variant of grey wolf optimizer. Int J Sci Technol Sci Iran. http://scientiairanica.sharif.edu.

  17. Teng, Z. J., Lv, J. L., & Guo, L. W. (2019). An improved hybrid grey wolf optimization algorithm. Soft Computing, 23(15), 6617–6631.

    Article  Google Scholar 

  18. Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion detection systems in wireless sensor networks: A review. International Journal of Distributed Sensor Networks, 9(5), 167575.

    Article  Google Scholar 

  19. Safaldin, M., Otair, M., & Abualigah, L. (2020). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 1–18.

  20. Islam, M. S., & Rahman, S. A. (2011). Anomaly intrusion detection system in wireless sensor networks: Security threats and existing approaches. International Journal of Advanced Science and Technology, 36(1), 1–8.

    Google Scholar 

  21. Tiwari, P., Saxena, V. P., Mishra, R. G., & Bhavsar, D. (2015). Wireless sensor networks: Introduction, advantages, applications and research challenges. HCTL Open International Journal of Technology Innovations and Research (IJTIR), 14, 1–11.

    Google Scholar 

  22. Ashoor, A. S., & Gore, S. (2011). Importance of intrusion detection system (IDS). International Journal of Scientific and Engineering Research, 2(1), 1–4.

    Google Scholar 

  23. Jyothsna, V. V. R. P. V., Prasad, V. R., & Prasad, K. M. (2011). A review of anomaly based intrusion detection systems. International Journal of Computer Applications, 28(7), 26–35.

    Article  Google Scholar 

  24. Sadek, R. A., Soliman, M. S., & Elsayed, H. S. (2013). Effective anomaly intrusion detection system based on neural network with indicator variable and rough set reduction. International Journal of Computer Science Issues (IJCSI), 10(6), 227.

    Google Scholar 

  25. Al-Jarrah, O. Y., Siddiqui, A., Elsalamouny, M., Yoo, P. D., Muhaidat, S., & Kim, K. (2014). Machine-learning-based feature selection techniques for large-scale network intrusion detection. In 2014 IEEE 34th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 177–181). IEEE.

  26. Chahal, J. K., & Kaur, A. (2016). A hybrid approach based on classification and clustering for intrusion detection system. International Journal of Mathematical Sciences & Computing, 2(4), 34–40.

    Article  Google Scholar 

  27. Malviya, V., & Jain, A. (2015). An efficient network intrusion detection based on decision tree classifier & simple k-mean clustering using dimensionality reduction–a review. International Journal on Recent and Innovation Trends in Computing and Communication, 3(2), 789–791.

    Article  Google Scholar 

  28. Shukla, V., & Vashishtha, S. (2014). New hybrid intrusion detection system based on data mining technique to enhanced performance. International Journal of Computer Science and Information Security, 12(6), 14.

    Google Scholar 

  29. Aljarah, I., & Ludwig, S. A. (2013). Mapreduce intrusion detection system based on a particle swarm optimization clustering algorithm. In 2013 IEEE Congress on Evolutionary Computation (pp. 955–962). IEEE.

  30. Duque, S., & Bin Omar, M. N. (2015). Using data mining algorithms for developing a model for intrusion detection system (IDS). Procedia Computer Science, 61, 46–51.

    Article  Google Scholar 

  31. Li, Z., Li, Y., & Xu, L. (2011). Anomaly intrusion detection method based on k-means clustering algorithm with particle swarm optimization. In 2011 International Conference of Information Technology, Computer Engineering and Management Sciences (Vol. 2, pp. 157–161). IEEE.

  32. http://wiki.analytica.com/Optimization_Characteristics

  33. Abd Rahman, M. A., Ismail, B., Naidu, K., & Rahmat, M. K. (2019). Review on population-based metaheuristic search techniques for optimal power flow. Indonesian Journal of Electrical Engineering and Computer Science, 15(1), 373–381.

    Article  Google Scholar 

  34. NSL-KDD Dataset. (n.d.). Canadian Institute for Cybersecurity. https://www.unb.ca/cic/datasets/nsl.html

  35. Dhanabal, L., & Shantharajah, S. P. (2015). A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering, 4(6), 446–452.

    Google Scholar 

  36. Dash, T. (2017). A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Computing, 21(10), 2687–2700.

    Article  Google Scholar 

  37. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  38. Guo, M. W., Wang, J. S., Zhu, L. F., Guo, S. S., & Xie, W. (2020). An improved grey wolf optimizer based on tracking and seeking modes to solve function optimization problems. IEEE Access, 8, 69861–69893.

    Article  Google Scholar 

  39. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks (Vol. 4, pp. 1942–1948). IEEE.

  40. Prabha, K. A., & Visalakshi, N. K. (2014). Improved particle swarm optimization based k-means clustering. In 2014 International Conference on Intelligent Computing Applications (pp. 59–63). IEEE.

  41. Umar, R., Mohammed, F., Deriche, M., & Sheikh, A. U. (2015). Hybrid cooperative energy detection techniques in cognitive radio networks. Handbook of research on software-defined and cognitive radio technologies for dynamic spectrum management (pp. 1–37). IGI Global.

    Google Scholar 

  42. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, No. 14, pp. 281–297).

  43. Morissette, L., & Chartier, S. (2013). The k-means clustering technique: General considerations and implementation in Mathematica. Tutorials in Quantitative Methods for Psychology, 9(1), 15–24.

    Article  Google Scholar 

  44. Younus, Z. S., Mohamad, D., Saba, T., Alkawaz, M. H., Rehman, A., Al-Rodhaan, M., & Al-Dhelaan, A. (2015). Content-based image retrieval using PSO and k-means clustering algorithm. Arabian Journal of Geosciences, 8(8), 6211–6224.

    Article  Google Scholar 

  45. Osuna, E., Freund, R., & Girosi, F. (1997). An improved training algorithm for support vector machines. In Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop (pp. 276–285). IEEE.

  46. Mukkamala, S., Janoski, G., & Sung, A. (2002). Intrusion detection: support vector machines and neural networks. In proceedings of the IEEE International Joint Conference on Neural Networks (ANNIE), St. Louis, MO (pp. 1702–1707).

  47. Tharwat, A. (2019). Parameter investigation of support vector machine classifier with kernel functions. Knowledge and Information Systems, 61(3), 1269–1302.

    Article  MathSciNet  Google Scholar 

  48. Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Mingcheng, G., Haixia, H., & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. Ieee Access, 6, 35365–35381.

    Article  Google Scholar 

  49. Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50.

    Article  Google Scholar 

  50. Ghasemi, J., Esmaily, J., & Moradinezhad, R. (2020). Intrusion detection system using an optimized kernel extreme learning machine and efficient features. Sādhanā, 45(1), 1–9.

    Article  Google Scholar 

  51. Odat, A., Otair, M., & Shehadeh, F. (2015). Image denoising by comprehensive median filter. International Journal of Applied Engineering Research, 10(15), 36016–36022.

    Google Scholar 

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Acknowledgements

This study was financially supported via a funding grant by Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/300), Taif University, Taif, Saudi Arabia

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Correspondence to Laith Abualigah.

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Otair, M., Ibrahim, O.T., Abualigah, L. et al. An enhanced Grey Wolf Optimizer based Particle Swarm Optimizer for intrusion detection system in wireless sensor networks. Wireless Netw 28, 721–744 (2022). https://doi.org/10.1007/s11276-021-02866-x

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