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Call for Papers:Vol.11 Issue.3

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Title: :  Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection
PaperId: :  25532
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 6 2024
DUI:    16.0415/IJARIIE-25532
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Manish K Alva’s institute of engineering and technology, Karnataka, India
Dr. Rachana P Alva’s institute of engineering and technology, Karnataka, India
Chandan M N Alva’s institute of engineering and technology, Karnataka, India
Laya RAlva’s institute of engineering and technology, Karnataka, India
Prashanth Kumar B CAlva’s institute of engineering and technology, Karnataka, India

Abstract

Information Science & Engineering
-
Network intrusion detection systems (NIDS) have been greatly enhanced by developments in machine learning (ML) and deep learning (DL), which have made it possible to analyze network traffic more effectively for anomaly identification. However, the sequential pattern of network transmission is frequently overlooked by current packet-based NIDS, increasing the number of false positives and negatives. Additionally, they usually ignore important header information, which makes it more difficult to identify assaults like denial-of-service (DoS). This research proposes a unique artificial intelligence-enabled paradigm for packet-based NIDS in order to overcome these constraints. By converting sequential packets into two-dimensional images, our technique records temporal linkages in addition to header and payload information. Malicious behavior is successfully identified by the suggested model using convolutional neural networks (CNN). Experiments on publicly accessible datasets show remarkable durability against adversarial instances and high detection rates (97.7%–99%) across a range of attack modes. These outcomes demonstrate our method's potential for precise, real-time intrusion detection in a variety of settings.

Citations

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IJARIIE Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. "Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 6 2024 Page 1930-1937
MLA Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. "Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection." International Journal Of Advance Research And Innovative Ideas In Education 10.6(2024) : 1930-1937.
APA Manish K , Dr. Rachana P , Chandan M N , Laya R, & Prashanth Kumar B C. (2024). Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection. International Journal Of Advance Research And Innovative Ideas In Education, 10(6), 1930-1937.
Chicago Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. "Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 6 (2024) : 1930-1937.
Oxford Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. 'Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, 2024, p. 1930-1937. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Transforming_Network_Security_by_Including_Convolutional_Neural_Networks_for_Improved_Automated_Attack_Classification_and_Real_Time_Intrusion_Detection_ijariie25532.pdf (Accessed : ).
Harvard Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. (2024) 'Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection', International Journal Of Advance Research And Innovative Ideas In Education, 10(6), pp. 1930-1937IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Transforming_Network_Security_by_Including_Convolutional_Neural_Networks_for_Improved_Automated_Attack_Classification_and_Real_Time_Intrusion_Detection_ijariie25532.pdf (Accessed : )
IEEE Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C, "Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 6, pp. 1930-1937, Nov-Dec 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Transforming_Network_Security_by_Including_Convolutional_Neural_Networks_for_Improved_Automated_Attack_Classification_and_Real_Time_Intrusion_Detection_ijariie25532.pdf [Accessed : ].
Turabian Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. "Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 6 ().
Vancouver Manish K , Dr. Rachana P , Chandan M N , Laya R, and Prashanth Kumar B C. Transforming Network Security by Including Convolutional Neural Networks for Improved Automated Attack Classification and Real-Time Intrusion Detection. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(6) : 1930-1937. Available from: https://ijariie.com/AdminUploadPdf/Transforming_Network_Security_by_Including_Convolutional_Neural_Networks_for_Improved_Automated_Attack_Classification_and_Real_Time_Intrusion_Detection_ijariie25532.pdf
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