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Network Intrusion Detection Model Based on PCA + ADASYN and XGBoost

Published: 24 March 2021 Publication History

Abstract

Due to the class-imbalance and redundancy of sample features, the network intrusion detection model based on classification algorithm has high false positive rate (FPR) for minority sample. A network intrusion detection model based on PCA + ADASYN and XGBoost is proposed. The principal component analysis (PCA) algorithm is used to reduce the redundancy features of the data. On this basis, the adaptive synthetic sampling (ADASYN) algorithm is used to oversample minority sample to solve the problem of class-imbalanced at the data level. Finally, XGBoost is used as a classifier to classify the detected data. In order to verify the validity of the model, several groups of comparative experiments were carried out on KDD CUP99 data set. The FPR of the proposed model for minority samples (r2l, u2r) were 17.3% and 19.7%, and the F1 were 90.1% and 84.5%. The experimental results show that by dealing with the problem of data redundancy and class-imbalanced, we can reduce the FPR of the detection model for minority sample and improve the F1.

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  • (2024)Abnormal traffic detection for Internet of Things based on an improved Residual NetworkPhysical Communication10.1016/j.phycom.2024.10240666(102406)Online publication date: Oct-2024
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  1. Network Intrusion Detection Model Based on PCA + ADASYN and XGBoost

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    cover image ACM Other conferences
    EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science
    December 2020
    718 pages
    ISBN:9781450389099
    DOI:10.1145/3453187
    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 ACM 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]

    In-Cooperation

    • Guilin: Guilin University of Technology, Guilin, China
    • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 March 2021

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    Author Tags

    1. ADASYN algorithm
    2. Class-imbalance
    3. Network intrusion detection
    4. PCA algorithm
    5. XGBoost

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    EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
    Overall Acceptance Rate 143 of 708 submissions, 20%

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    Cited By

    View all
    • (2024)DDoS attacks detection using different decision tree algorithmsi-manager's Journal on Computer Science10.26634/jcom.12.2.2110812:2(28)Online publication date: 2024
    • (2024)Enhancing Rainfall Prediction Accuracy through XGBoost Model with Data Balancing Techniques2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)10.1109/CSPA60979.2024.10525558(120-125)Online publication date: 1-Mar-2024
    • (2024)Abnormal traffic detection for Internet of Things based on an improved Residual NetworkPhysical Communication10.1016/j.phycom.2024.10240666(102406)Online publication date: Oct-2024
    • (2023)Analyzing DDoS Attack Classification with Data Imbalance Using Generative Adversarial Networks2023 IEEE Latin-American Conference on Communications (LATINCOM)10.1109/LATINCOM59467.2023.10361882(1-6)Online publication date: 15-Nov-2023
    • (2022)A Systematic Review on Hybrid Intrusion Detection SystemSecurity and Communication Networks10.1155/2022/96630522022Online publication date: 1-Jan-2022
    • (2022)Dealing with Imbalanced Data in Multi-class Network Intrusion Detection Systems Using XGBoostMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-030-93733-1_1(5-21)Online publication date: 18-Feb-2022
    • (2021)SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLANIEEE Access10.1109/ACCESS.2021.31296009(157639-157653)Online publication date: 2021

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