research-article

Intrusion Detection Based on Approximate Information Entropy for Random Forest Classification

Authors:

Le Yang,

Manchun Cai,

Yongcheng Duan,

Xue YangAuthors Info & Claims

ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

Pages 125 - 129

https://doi.org/10.1145/3335484.3335488

Published: 10 May 2019 Publication History

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Abstract

Aiming at the classification detection problem of intrusion detection system, this paper proposes a classification algorithm based on approximate information entropy and random forest. First, the training data is subjected to dimensionality reduction and noise reduction by approximating information entropy, and redundant attributes are deleted. Secondly, the processed data is classified using the random forest classification algorithm. Finally, in order to verify the effectiveness of the algorithm, the improved method was tested using the KDD-CUP99 data set. The experimental results show that the data with approximate information entropy dimensionality reduction and noise reduction can effectively reduce the time complexity of classification and improve the classification accuracy.

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Cited By

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Al Shahriar MDey A(2023)A Hybrid Approach of CNN and LSTM to Detect Intrusion in Edge IoT Devices using CatBoost2023 26th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT60459.2023.10441595(1-6)Online publication date: 13-Dec-2023
https://doi.org/10.1109/ICCIT60459.2023.10441595
Abbas AKhan MLatif SAjaz MShah AAhmad J(2021)A New Ensemble-Based Intrusion Detection System for Internet of ThingsArabian Journal for Science and Engineering10.1007/s13369-021-06086-547:2(1805-1819)Online publication date: 30-Aug-2021
https://doi.org/10.1007/s13369-021-06086-5
Ajdani MGhaffary H(2021) Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm SECURITY AND PRIVACY10.1002/spy2.1474:2Online publication date: 24-Jan-2021
https://doi.org/10.1002/spy2.147

Index Terms

Intrusion Detection Based on Approximate Information Entropy for Random Forest Classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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Published In

ICBDC '19: Proceedings of the 4th International Conference on Big Data and Computing

May 2019

353 pages

ISBN:9781450362788

DOI:10.1145/3335484

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]

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Shenzhen University: Shenzhen University
Sun Yat-Sen University

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Association for Computing Machinery

New York, NY, United States

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Published: 10 May 2019

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ICBDC 2019: 2019 4th International Conference on Big Data and Computing

May 10 - 12, 2019

Guangzhou, China

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Al Shahriar MDey A(2023)A Hybrid Approach of CNN and LSTM to Detect Intrusion in Edge IoT Devices using CatBoost2023 26th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT60459.2023.10441595(1-6)Online publication date: 13-Dec-2023
https://doi.org/10.1109/ICCIT60459.2023.10441595
Abbas AKhan MLatif SAjaz MShah AAhmad J(2021)A New Ensemble-Based Intrusion Detection System for Internet of ThingsArabian Journal for Science and Engineering10.1007/s13369-021-06086-547:2(1805-1819)Online publication date: 30-Aug-2021
https://doi.org/10.1007/s13369-021-06086-5
Ajdani MGhaffary H(2021) Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm SECURITY AND PRIVACY10.1002/spy2.1474:2Online publication date: 24-Jan-2021
https://doi.org/10.1002/spy2.147

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Reinforced random forest

Network intrusion detection using Machine Learning approach

Rotation Forest: A New Classifier Ensemble Method

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