ml-experiments-iot23

ML experiments with IoT23 dataset [1]

1. (Step 0) Prerequisites (Tools & Technologies)

No	Name	Version	Description
1	Python	3.8.8	Programming Language
2	scikit-learn	0.24.1	Tools for Machine Learning in Python
3	NymPy	1.19.5	Tools for Scientific Computing in Python
4	pandas	1.2.2	Tools for Data Analysis & Data Manipulation in Python
5	matplotlib	3.3.4	Visualization with Python
6	seaborn	0.11.1	Statistical data visualization
7	psutil	5.8.0	Cross-platform library for retrieving information on running processes and system utilization (CPU, memory, disks, network, sensors) in Python
8	scikit-plot	0.3.7	Library for visualizations
9	pickle	-	Python object serialization for model serialization

2. (Step 1) Configure Project

1. Download & Extract IoT23
2. Clone this repo
3. Install missing libraries
4. Open config.py and configure required directories

• iot23_scenarios_dir should point to the home folder, where iot23 scenarios are located
• iot23_attacks_dir will be used to store files for each attack type from the scenarios files
• iot23_experiments_dir will be used to store experiment files, including trained models and results

5. Run configuration check by running run_configuration_check.py

Make sure the output message says that you may continue with the next step. If not, then check your configuration and fix the errors.

3. (Step 2) Extract Attacks from Scenarios

1. Run data extraction by running run_data_extraction_from_scenarios.py

Even though, there are multiple scenarios, files still contain mixed attack and benign traffic. For this reason we are going to extract the entries of a similar type into separate files. The output files will be stored in iot23_attacks_dir.

4. (Step 2) Run Demo

1. Run demo by running run_demo.py

5. (Step 3) Run Experiments

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[1] “Stratosphere Laboratory. A labeled dataset with malicious and benign IoT network traffic. January 22th. Agustin Parmisano, Sebastian Garcia, Maria Jose Erquiaga. Online: https://www.stratosphereips.org/datasets-iot23