It has come to my attention that my research paper, titled "Machine Learning and Deep Learning Methods for Better Anomaly Detection in IoT-23 Dataset Cybersecurity", which is available in this repository, has been plagiarized and published without my consent.
The plagiarized paper, "The Detection of IoT Botnet using Machine Learning on IoT-23 Dataset", was published on IEEE Xplore by individuals who were not involved in the original research or project. You can find the link to the plagiarized paper here: https://ieeexplore.ieee.org/document/9754187.
I am currently taking appropriate action by reporting this violation to IEEE and other relevant authorities. I will provide further updates as the situation progresses.
If you have any questions or additional information regarding this matter, please feel free to reach out or open an issue on this repository. Thank you for your support.
Best regards,
Yue (Dylan) Liang
September 09, 2024
This project is part of the research under
Machine Learning and Deep Learning Methods for Better Anomaly Detection in IoT-23 Dataset Cybersecurity.
- The goal of the research was to find the best solution based on time efficiency and accuracy.
- This paper proposed an anomaly detection system model for IoT security with the implementation of ML/DL methods, including Naïve Bayes, SVM, Decision Trees, and CNN.
- The proposed method reached better accuracy compared to other paper.
- The research was performed on the IoT-23 dataset.
This file is the data preprocessing for IoT-23 dataset. It loads 23 datasets seprately into Pandas dataframe, then skip the first 10 rows (headers) and load the 100,000 rows after. When finished, it combines 23 dataframes into a new dataset: iot23_combined.csv
Note: The lighter version (8.8GB) of IoT-23 dataset was used in this research.
There are total of 4 models are implemented in this project:
- CNN
- SVM
- Decision Trees
- Navie Bayes
Anaconda Jupyter Notebook
Python 3.8
Tensorflow 2.4
Stratosphere Laboratory. A labeled dataset with malicious and benign IoT network traffic. January 22th. Agustin Parmisano, Sebastian Garcia, Maria Jose Erquiaga.
https://www.stratosphereips.org/datasets-iot23