Data Poisoning Attacks on Federated Using Machine Learning
DOI:
https://doi.org/10.32628/IJSRST251257Keywords:
Data poisoning, Machine learningAbstract
Data poisoning attacks are a type of adversarial attack that aims to corrupt the training data used to build machine learning models. In this study, we investigate the effectiveness of data poisoning attacks on Three popular machine learning algorithms: SVM, PCA and Naïve Bayes, and Decision We propose a novel data poisoning attack that selectively manipulates training data to induce Miss Classification. Our attack strategy involves injecting a small number of Malicious examples that are designed to bias the decision boundaries of the classifiers towards a specific class. Our experimental results demonstrate that our attack strategy is effective and can significantly degrade the performance of the targeted classifiers. Specifically, our attack achieves a success rate of up to 90% on SVM, PCA and Naïve Bayes, and up to 70% on Decision Tree. Furthermore, we show that our attack is robust to various defenses, including outlier removal and regularization. Our findings highlight the vulnerability of machine learning models to data poisoning attacks and emphasize the need for developing robust and secure machine learning algorithms
Downloads
References
JafarAbo Nada; Mohammad Rasmi Al-Mosa, 2018 International Arab Conference on Information Technology (ACIT), A Proposed Wireless Intrusion Detection Prevention and Attack System DOI: https://doi.org/10.1109/ACIT.2018.8672722
Kinam Park; Youngrok Song; Yun-Gyung Cheong, 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigData Service), Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm DOI: https://doi.org/10.1109/BigDataService.2018.00050
S. Bernard, L. Heutte and S. Adam “On the Selection of Decision Trees in Random Forests” Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14- 19, 2009, 978-1-4244-3553-1/09/$25.00 ©2009 IEEE
A.Tesfahun, D. Lalitha Bhaskari, “Intrusion Detection using Random Forests Classifier with SMOTE and Feature Reduction” 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, 978-0-4799-2235-2/13 $26.00 © 2013 IEEE
Le, T.-T.-H., Kang, H., & Kim, H. (2019). The Impact of PCA-Scale Improving GRU Performance for Intrusion Detection. 2019 International Conference on Platform Technology and Service (PlatCon). Doi:10.1109/platcon.2019.8668960 DOI: https://doi.org/10.1109/PlatCon.2019.8668960
Anish Halimaa A, Dr K.Sundarakantham: Proceedings of the Third International Conference on Trends in Electronics and Informatics (ICOEI 2019) 978-1-5386-9439- 8/19/$31.00 ©2019 IEEE “MACHINE LEARNING BASED INTRUSION DETECTION SYSTEM
Mengmeng Ge, Xiping Fu, Naeem Syed, Zubair Baig, Gideon Teo,AntonioRoblesKelly (2019). Deep Learning-Based Intrusion Detect ion for IoT Networks, 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), pp. 256-265, Japan. DOI: https://doi.org/10.1109/PRDC47002.2019.00056
R. Patgiri, U. Varshney, T. Akutota, and R. Kunde, “An Investigation on Intrusion Detection System Using Machine Learning” 978-1-5386-9276-9/18/$31.00 c2018IEEE.
Rohit Kumar Singh Gautam, Er. Amit Doegar; 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) “An Ensemble Approach for Intrusion Detect ion System Using Machine Learning Algorithms.” DOI: https://doi.org/10.1109/CONFLUENCE.2018.8442693
Kazi Abu Taher, Billal Mohammed Yasin Jisan, Md. Mahbubur Rahma, 2019 International Conference on Robot ics, Electrical and Signal Processing Techniques (ICREST)“Network Intrusion Detect ion using Supervised Machine Learning Technique with Feature Selection. DOI: https://doi.org/10.1109/ICREST.2019.8644161
L.Haripriya, M.A. Jabbar, 2018 Second International Conference on Electronics,communication and aerospace Technology(ICECA Role of machine kearning in intrusion Detection system Review . DOI: https://doi.org/10.1109/ICECA.2018.8474576
Nimmy Krishnan, A. Salim, 2018 International CET Conference on control, communication and computing (IC4) “Machine Learning- Based intrusion Detect ion for virtualized Infrastructures”. DOI: https://doi.org/10.1109/CETIC4.2018.8530912
Mohammed Ishaque ,Ladislav hudec ,2019 2nd International confernence on computer Applications and information security (ICCAIS) “ Feature extract ion using Deep Learning for intrusion Detection System ”. DOI: https://doi.org/10.1109/CAIS.2019.8769473
Aditiya Phadke ,Mohit Kulkarni , Parnav Bhawalkar, Raashmi Bhattad, 2019 3rd International conference on computing Methodologies and Communication(ICCMC) “ A Review of Machine Learning Methodologies for Network Intrusion Detection.” DOI: https://doi.org/10.1109/ICCMC.2019.8819748
Iftikar Ahamed , Mohammad Basheri, Muhammed Javed Iqbal, Aneel Rahim, IEEE Access( Volume 6) Pages (s): 33789 “ Performance comparsion of support Vector Machine, Random forest and Extreme Learning Machine for Instrusion Detection .” DOI: https://doi.org/10.1109/ACCESS.2018.2841987
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
