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Fusion: Practice and Applications
Volume 13 , Issue 1, PP: 117-125 , 2023 | Cite this article as | XML | Html |PDF

Title

Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset

  Manjunath H. 1 * ,   Saravana Kumar 2

1  Department of Computer Science and Engineering, CMR University, Bangalore, India
    (manjunath.19cphd@cmr.edu.in)

2  Department of Computer Science and Engineering, CMR University, Bangalore, India
    (sarvana.k@cmr.edu.in)

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Doi   :   https://doi.org/10.54216/FPA.130109

Received: March 24, 2023 Revised: June 27, 2023 Accepted: September 02, 2023

Abstract :

Increase in network activity of transferring information online allows network breeches where intruders easily avail the most important information or data. The growth of online functioning and many other governmental data over the internet without security has caused data vulnerability; attackers can easily detect the data and misuse them. Network Intrusion Detection System (NIDS) has allowed this whole process of online data transfer to occur safely and secured transactions. Due to the cloud usage in network the huge amount of traffic is created as well as number of attacks are increased day by day. To prevent the vulnerability and its types are social, environmental, cognitive, military attacks in the network are classified using CRNN model.  We used ensemble learning methods in machine learning algorithms are used to detect and prevent the malicious packets in the network. Our model detects the unauthorized users intruding into any network and alerts the organization regarding the same. When a typical firewall is unable to effectively stop certain sorts of attacks on computer system usage and network communications, a network intrusion detection system may be used. First, we are classifying the unauthorized packets using machine learning algorithm. Using our concept, we have used neural networks in this paper to detect any such attack. For the Network Security Laboratory - Knowledge Discovery in Databases data set using CNN and RNN algorithms, we also applied a few well-known techniques as boosting and pasting methods. In this CRNN approach, we demonstrate that neural networks are more effective than other methods at detecting attacks.

Keywords :

Neural networks; supervised learning; Network Security Laboratory - Knowledge Discovery in Databases; SVM and Random Forest; CRNN- convolutional and recurrent neural network.

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Cite this Article as :
Style #
MLA Manjunath H., Saravana Kumar. "Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset." Fusion: Practice and Applications, Vol. 13, No. 1, 2023 ,PP. 117-125 (Doi   :  https://doi.org/10.54216/FPA.130109)
APA Manjunath H., Saravana Kumar. (2023). Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset. Journal of Fusion: Practice and Applications, 13 ( 1 ), 117-125 (Doi   :  https://doi.org/10.54216/FPA.130109)
Chicago Manjunath H., Saravana Kumar. "Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset." Journal of Fusion: Practice and Applications, 13 no. 1 (2023): 117-125 (Doi   :  https://doi.org/10.54216/FPA.130109)
Harvard Manjunath H., Saravana Kumar. (2023). Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset. Journal of Fusion: Practice and Applications, 13 ( 1 ), 117-125 (Doi   :  https://doi.org/10.54216/FPA.130109)
Vancouver Manjunath H., Saravana Kumar. Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset. Journal of Fusion: Practice and Applications, (2023); 13 ( 1 ): 117-125 (Doi   :  https://doi.org/10.54216/FPA.130109)
IEEE Manjunath H., Saravana Kumar, Network Intrusion Detection System using Convolution Recurrent Neural Networks and NSL-KDD Dataset, Journal of Fusion: Practice and Applications, Vol. 13 , No. 1 , (2023) : 117-125 (Doi   :  https://doi.org/10.54216/FPA.130109)
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