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Research on Network Intrusion Detection Techniques Based on Feature Selection Model and Recurrent Neural Network

Published: 28 December 2023 Publication History

Abstract

The number of network attacks has also increased rapidly. Therefore, it is necessary to conduct in-depth research on network intrusion detection technologies. Compared with traditional intrusion detection systems, AI-based intrusion detection systems can better detect network traffic, with lower false positive and false negative rates. This paper first expanded the CIC-IDS-2017 dataset by adding two new attack forms. Secondly, the SMOTE algorithm was used to expand the minority samples in the dataset. Through the sequence forward selection algorithm based on decision trees, the features in the dataset were selected, improving the algorithm's efficiency without significantly affecting its accuracy. Finally, a multi-task network intrusion detection model was constructed by integrating the prediction results of the recurrent neural network model and the one-class support vector machine model to determine the final network traffic type. The system has been quite successful in achieving all of its initial goals at this stage of research. The system achieved a detection accuracy of over 90% for network traffic can predict unknown types of attack traffic. The real-time detection function allows the system to be applied to practical network traffic detection on a daily basis.

References

[1]
Liu J H, Zhang A L, Huang Z Q, Huang D Y, Chen X W. Optimization and dimensionality reduction analysis of CSE-CIC-IDS2018 intrusion detection dataset based on machine learning. Fire and Command&Control, 2021: 155-162
[2]
Sharafaldin I, Lashkari A H, Ghorbani A A A Detailed Analysis of the CICIDS2017 Data Set Springer, Cham, 2018:1057-1072
[3]
Wang L M. Research on Network Intrusion Detection Algorithm Based on Machine Learning. Beijing Jiaotong University, 2020:158-171
[4]
Hu L W. Research and Implementation of Deep Learning Method for Malicious Traffic. Identification Based on Multi task. Nanjing University of Posts and Telecommunications, 2021:234-246
[5]
Ye Q, Tan T, Sun Y J. An overview of intrusion detection systems based on deep learning. Information Security and Communication Privacy, 2021: 96-104
[6]
Sajid A. Analysis of intrusion detection system based on machine learning and deep learning .Technology NCEPU, 2021:314-318
[7]
Ranjit P, Samarjeet B. A detailed analysis of CICIDS2017 data set for designing. Intrusion Detection Systems International Journal of Engineering&Technology, January 2018: 479-482
[8]
Zhang H, Zhang X Y, Zhang Z Y, Li W. An overview of intrusion detection models based on deep learning. Computer Engineering and Applications, 2022: 17-28
[9]
Li L J, Li M, Bi H J, Zhou H C. Multi type low rate DDoS attack detection method based on hybrid deep learning. Journal of Network and Information Security, 2022: 73-85
[10]
Yang J. Research on real-time attack detection technology based on network flow characteristics. Beijing University of Posts and Telecommunications, 2021:251-263
[11]
Ghazaros B Y, Yu Y Y, Manawa A. Model for detection of masquerade attacks based on variable-length sequences. IEEE Access. 2020: 210140–210157
[12]
Le T H, Kim Y, Kim H. Network intrusion detection based on novel feature selection model and various recurrent neural networks. Appl. Sci. 2019; 1392
[13]
Zhang N, Deng S, Sun Z, Chen X, Zhang X, Chen H. Attentionbased capsule networks with dynamic routing for relation extraction. Proc. Conf. Empirical Methods Natural Lang. Process. 2018. pp. 986–992
[14]
Liu X M, Yue J L. Real-time anomaly attack detection based on an improved variable length model, Journal of Computational Methods in Sciences and Engineering. 2023:1179–1195
[15]
Sabour S, Frosst N, Hinton GE. Dynamic routing between capsules. Proc. 31st Int. Conf, Neural Inf. Process. 2017. pp. 3859–3869
[16]
Lu Q C. Design and implementation of Linux host intrusion detection system. Huazhong University of science and technology. 2019; 310–321
[17]
Jeremy J; Kelly B. Global Cybersecurity Forum. World Economic Forum, 2022:544-558
[18]
Le T T H, Kim Y, Kim H Network Intrusion Detection Based on New Feature Selection Model and Various Recurrent Neural Networks Applied Sciences, 2019:212-225
[19]
Ranjit P, Samarjeet B. A detailed analysis of CICIDS2017 data set for designing. Intrusion Detection Systems International Journal of Engineering&Technology, January 2018: 479-482
[20]
Sharafaldin I, Lashkari A H, Ghorbani A A. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. International Conference on Information Systems Security. 2022:310-323
[21]
Sajid A. Analysis of intrusion detection system based on machine learning and deep learning technology. NCEPU, 2021:1045-1059
[22]
Kamil Z, Yusof R, Bahman N, Benchmarking of Machine Learning for Anomaly Based. Intrusion Detection Systems in the CICIDS2017 Dataset IEEE Access, 2021:105-123

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            ICCSIE '23: Proceedings of the 8th International Conference on Cyber Security and Information Engineering
            September 2023
            370 pages
            ISBN:9798400708800
            DOI:10.1145/3617184
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            Published: 28 December 2023

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            Author Tags

            1. CIC-IDS-2017 dataset
            2. Feature selection models
            3. Network intrusion detection
            4. Recurrent neural network

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            • National Natural Fund

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            ICCSIE 2023

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