International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Novel CNN and GA-Based Algorithm for Intrusion Detection in IoT Devices

  • Received : 2023.09.05
  • Published : 2023.09.30
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Abstract

The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST21 and KDDTEST+ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST+ than KDDTEST21 because there are few attacks from minor samples in KDDTEST+. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.

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References

  1. B. B. Zarpelao, R. S. Miani, C. T. Kawakani, and S. C. de Alvarenga, "A survey of intrusion detection in Internet of Things," Journal of Network and Computer Applications, vol. 84, pp. 25-37, 2017.  https://doi.org/10.1016/j.jnca.2017.02.009
  2. S. Pundir, M. Wazid, D. P. Singh, A. K. Das, J. J. Rodrigues, and Y. Park, "Intrusion detection protocols in wireless sensor networks integrated to the Internet of Things deployment: Survey and future challenges," IEEE Access, vol. 8, pp. 3343-3363, 2019.  https://doi.org/10.1109/ACCESS.2019.2962829
  3. E. Benkhelifa, T. Welsh, and W. Hamouda, "A critical review of practices and challenges in intrusion detection systems for IoT: Toward universal and resilient systems," IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 3496-3509, 2018. 
  4. H. Wang, Z. Cao, and B. Hong, "A network intrusion detection system based on convolutional neural network," Journal of Intelligent & Fuzzy Systems, no. Preprint, pp. 1-15, 2019. 
  5. Y. Liu, S. Liu, and X. Zhao, "Intrusion detection algorithm based on convolutional neural network," DEStech Transactions on Engineering and Technology Research, no. iceta, 2017. 
  6. X. Yang and Z. Hui, "Intrusion detection alarm filtering technology based on ant colony clustering algorithm," in 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA), 2015: IEEE, pp. 470-473. 
  7. C. Yin, Y. Zhu, J. Fei, and X. He, "A deep learning approach for intrusion detection using recurrent neural networks," Ieee Access, vol. 5, pp. 21954-21961, 2017.  https://doi.org/10.1109/ACCESS.2017.2762418
  8. W. L. Al-Yaseen, Z. A. Othman, and M. Z. A. Nazri, "Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system," Expert Systems with Applications, vol. 67, pp. 296-303, 2017.  https://doi.org/10.1016/j.eswa.2016.09.041
  9. H. Ji, D. Kim, D. Shin, and D. Shin, "A Study on comparison of KDD CUP 99 and NSL-KDD using artificial neural network," in Advances in computer science and ubiquitous computing: Springer, 2017, pp. 452-457. 
  10. B. Selvakumar and K. Muneeswaran, "Firefly algorithm based feature selection for network intrusion detection," Computers & Security, vol. 81, pp. 148-155, 2019. 
  11. E. Min, J. Long, Q. Liu, J. Cui, and W. Chen, "TR-IDS: Anomaly-based intrusion detection through a text-convolutional neural network and random forest," Security and Communication Networks, vol. 2018, 2018. 
  12. L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, "IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?," IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 41-49, 2018.  https://doi.org/10.1109/MSP.2018.2825478
  13. B. Riyaz and S. Ganapathy, "A deep learning approach for effective intrusion detection in wireless networks using CNN," Soft Computing, pp. 1-14, 2020. 
  14. J. Kim, J. Kim, H. Kim, M. Shim, and E. Choi, "CNN-Based Network Intrusion detection against Denial-ofService Attacks," Electronics, vol. 9, no. 6, p. 916, 2020. 
  15. B. Susilo and R. F. Sari, "Intrusion detection in IoT Networks Using Deep Learning Algorithm," Information, vol. 11, no. 5, p. 279, 2020. 
  16. J. Jeon, J. H. Park, and Y.-S. Jeong, "Dynamic Analysis for IoT Malware Detection with Convolution Neural Network model," IEEE Access, 2020. 
  17. M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, "Deep recurrent neural network for IoT intrusion detection system," Simulation Modelling Practice and Theory, vol. 101, p. 102031, 2020. 
  18. D. Zheng, Z. Hong, N. Wang, and P. Chen, "An improved LDA-based ELM classification for intrusion detection algorithm in IoT application," Sensors, vol. 20, no. 6, p. 1706, 2020. 
  19. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105. 
  20. L. Dhanabal and S. Shantharajah, "A study on NSL-KDD dataset for intrusion detection system based on classification algorithms," International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 6, pp. 446-452, 2015. 
  21. H. Yang and F. Wang, "Wireless network intrusion detection based on improved convolutional neural network," IEEE Access, vol. 7, pp. 64366-64374, 2019.