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Title: :  Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks
PaperId: :  19089
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 1 2023
DUI:    16.0415/IJARIIE-19089
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Abhishek Gokavarapuraghu institute of technology
DR. P.M. Manoharraghu institute of technology
B.S.Pandaraghu institute of technology

Abstract

computer engineering
Deep Learning, Cyber security, Intrusion detection TensorFlow, Keras, Python, OpenCV.
Software Defined Networking (SDN) is a promising technology for the future Internet. However, the Intrusion detection paradigm introduces new attack vectors that do not exist in the conventional distributed networks. This paper develops a hybrid Intrusion Detection System (IDS) by combining the Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). The proposed model is capable of capturing the spatial and temporal features of the network traffic. Two regularization techniques i.e., L2 Regularization (L2Reg.) and dropout method are used to overcome with the overfitting problem. The proposed method improves the intrusion detection performance of zero-day attacks. The In KDD cup dataset — the most recent dataset for NSL-KDD networks is used to test and evaluate the performance of the proposed model. The results indicate that integrating the CNN with LSTM improves the intrusion detection performance and achieves an accuracy of 96.32%. The estimated accuracy is higher than the accuracy of each individual model. In addition, it is established that the regularization techniques improve the performance of the CNN algorithms in detecting new intrusions when compared to the standard CNN+LSTM. The findings of this study facilitate the development of robust IDS systems for SDN environment.

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IJARIIE Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. "Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 1 2023 Page 727-734
MLA Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. "Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education 9.1(2023) : 727-734.
APA Abhishek Gokavarapu, DR. P.M. Manohar, & B.S.Panda. (2023). Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks. International Journal Of Advance Research And Innovative Ideas In Education, 9(1), 727-734.
Chicago Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. "Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 1 (2023) : 727-734.
Oxford Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. 'Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 1, 2023, p. 727-734. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Detecting_anomaly_based_network_intrusions_by_using_hybrid_architectures_of_Convolutional_Neural_Networks_ijariie19089.pdf (Accessed : ).
Harvard Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. (2023) 'Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks', International Journal Of Advance Research And Innovative Ideas In Education, 9(1), pp. 727-734IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Detecting_anomaly_based_network_intrusions_by_using_hybrid_architectures_of_Convolutional_Neural_Networks_ijariie19089.pdf (Accessed : )
IEEE Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda, "Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 1, pp. 727-734, Jan-Feb 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Detecting_anomaly_based_network_intrusions_by_using_hybrid_architectures_of_Convolutional_Neural_Networks_ijariie19089.pdf [Accessed : ].
Turabian Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. "Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 1 ().
Vancouver Abhishek Gokavarapu, DR. P.M. Manohar, and B.S.Panda. Detecting anomaly-based network intrusions by using hybrid architectures of Convolutional Neural Networks. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(1) : 727-734. Available from: https://ijariie.com/AdminUploadPdf/Detecting_anomaly_based_network_intrusions_by_using_hybrid_architectures_of_Convolutional_Neural_Networks_ijariie19089.pdf
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