Abstract
Intrusion detection technology is the key technology in network security. With the diversification of means of network attacks, the traditional intrusion detection technology has gradually revealed some problems, such as poor detection performance and low adaptability. In view of the existing problems, this paper constructs an intrusion detection model based on improved convolution neural network. The convolution neural network is further studied and improved. This paper optimizes the initial weights of convolutional neural network by genetic algorithm on the problems of slow training speed and difficult convergence in the training of convolutional neural network. The experimental results show that the convergence speed of the convolution neural network optimized by genetic algorithm is faster and the feature extraction ability is strengthened. Convolution neural network based on genetic algorithm can detect various kinds of abnormal data and attack types effectively, and also has the ability to detect new attack data.
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References
Qin, S., Jiang, J., Ma, H., et al.: Research on intrusion detection techniques: a survey. Sci. Technol. Inf. 7, 19–29 (2004). (in Chinese)
Guo, C., Zhou, Y., Ping, Y., et al.: A distance sum-based hybrid method for intrusion detection. Appl. Intell. 40(1), 178–188 (2014)
Bostani, H., Sheikhan, M.: Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept. Pattern Recogn. 62, 56–72 (2017)
Kumar, G.R., Mangathayaru, N., Narsimha, G.: An approach for intrusion detection using novel Gaussian based Kernel function. J. Univ. Comput. Sci. 22(4), 589–604 (2016)
Dang, B.H., Li, W.: Performance evaluation of unsupervised learning techniques for intrusion detection in mobile Ad Hoc networks. In: Lee, R. (ed.) Computer and Information Science. Studies in Computational Intelligence, vol. 566, pp. 71–86. Springer, Cham (2015)
Murthy, Y.V.S., Harish, K., Varma, D.K.V., et al.: Hybrid intelligent intrusion detection system using Bayesian and genetic algorithm (BAGA): comparitive study. Int. J. Comput. Appl. 99(2), 1–8 (2014)
Le Cun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2015)
Eesa, A.S., Orman, Z., Brifcani, A.M.A.: A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Syst. Appl. 24(5), 2670–2679 (2015)
Wang, L., Jones, R.: Big data analytics for network intrusion detection: a survey. Int. J. Netw. Commun. 7(1), 24–31 (2017)
Amoli, P.V., Hamalainen, T., David, G., et al.: Unsupervised network intrusion detection systems for zero-day fast-spreading attacks and botnets. Int. J. Digit. Content Technol. Appl. 10(2), 1–13 (2016)
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Li, S. (2020). Network Intrusion Detection Model Based on Improved Convolutional Neural Network. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-030-43306-2_3
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DOI: https://doi.org/10.1007/978-3-030-43306-2_3
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Online ISBN: 978-3-030-43306-2
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