Anomaly Detection IoT23

Research Paper

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.

Data Preprocessing

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.

Models

There are total of 4 models are implemented in this project:

• CNN
• SVM
• Decision Trees
• Navie Bayes

Environment Settings

Anaconda Jupyter Notebook
Python 3.8
Tensorflow 2.4

IoT-23 Dataset

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