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Title: :  Analysis of network intrusion detection using feature engineering in ML
PaperId: :  22792
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22792
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
G Tejaswini Vasireddy venkatadri institute of technology
Dasari TejaswiVasireddy venkatadri institute of technology
Jogu KavyaVasireddy venkatadri institute of technology
Bulla KavithaVasireddy venkatadri institute of technology
Ameesha ShaikVasireddy venkatadri institute of technology
Tadi ShaliniVasireddy venkatadri institute of technology

Abstract

Machine Learning
Machine Learning, Intrusion Detection System, Cybersecurity and Feature Engineering etc…
Intrusion detection Systems (IDS) play a critical role in safeguarding computer networks from malicious activities. This project aims to enhance IDS performance through the integration of feature engineering, specifically Recursive Feature Elimination (RFE), coupled with the Random Forest algorithm. Feature engineering involves selecting and transforming input variables to improve model performance. RFE, a subset selection technique, iteratively removes less significant features, enhancing model efficiency and interpretability. Random Forest, a powerful ensemble learning method, leverages decision trees to classify data, offering robustness and accuracy in complex datasets. The evaluation metrics include accuracy, precision, recall, F1-score, and Cross Validation graphs. Experimental results will demonstrate the efficiency of the proposed approach in improving IDS performance in terms of detection accuracy and efficiency. This project contributes to the field of cybersecurity by providing insights into the effectiveness of feature engineering techniques, particularly RFE, in enhancing IDS capabilities. Ultimately, this project seeks to contribute to the advancement of intrusion detection techniques, offering insights into effective feature selection strategies and algorithmic frameworks for enhancing network security in contemporary computing environments.

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IJARIIE G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. "Analysis of network intrusion detection using feature engineering in ML" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 698-705
MLA G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. "Analysis of network intrusion detection using feature engineering in ML." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 698-705.
APA G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, & Tadi Shalini. (2024). Analysis of network intrusion detection using feature engineering in ML. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 698-705.
Chicago G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. "Analysis of network intrusion detection using feature engineering in ML." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 698-705.
Oxford G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. 'Analysis of network intrusion detection using feature engineering in ML', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 698-705. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Analysis_of_network_intrusion_detection_using_feature_engineering_in_ML_ijariie22792.pdf (Accessed : ).
Harvard G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. (2024) 'Analysis of network intrusion detection using feature engineering in ML', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 698-705IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Analysis_of_network_intrusion_detection_using_feature_engineering_in_ML_ijariie22792.pdf (Accessed : )
IEEE G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini, "Analysis of network intrusion detection using feature engineering in ML," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 698-705, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Analysis_of_network_intrusion_detection_using_feature_engineering_in_ML_ijariie22792.pdf [Accessed : ].
Turabian G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. "Analysis of network intrusion detection using feature engineering in ML." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver G Tejaswini , Dasari Tejaswi, Jogu Kavya, Bulla Kavitha, Ameesha Shaik, and Tadi Shalini. Analysis of network intrusion detection using feature engineering in ML. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 698-705. Available from: https://ijariie.com/AdminUploadPdf/Analysis_of_network_intrusion_detection_using_feature_engineering_in_ML_ijariie22792.pdf
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