Urban_IoT_DDoS_Data

This repository presents the source code for analyzing an Urban IoT Activity Dataset. It helps with generating attacks on the datasets and a neural network model for detecting them.

Instructions for running the codes

The requirements.txt file contains the modules needed to run these scripts and can be installed by running the following command in the terminal:

• pip install -r requirements.txt

Project config file

The project config file can be found in /source_code. The path to the output directory can be set in this file.

Cleaning the dataset

Before running any code, the original dataset need to be unzip in the /dataset directory. One python scripts can be found in /source_code/clean_dataset folder for pre-processing the original dataset.

clean_dataset.py

This file genrates the bening_dataset which has N nodes and each node has time entries starting from the beginning to the end of original dataset with a step of time_step.

Input:

• Input Dataset
• Number of nodes
• Timestep

Output:

• Benign dataset

Dataset Statistics

Three python scripts can be found in /source_code/stats folder for generating the statistics of the benign dataset.

active_nodes_percentage.py

This script generates the plots of active nodes percentage vs time.

Input:

• Benign dataset

Output:

• Plots of active nodes percentage vs time.

correlation.py

This script generates the plot of Pearson correlation of nodes vs their distance.

Input:

• Benign dataset

Output:

• Plot of Pearson correlation of nodes vs their distance

nodes_active_mean_time.py

This script generates the histograms of nodes active and inactive mean time vs time of the day.

Input:

• Benign dataset

Output:

• Plot histograms of nodes active and inactive mean time vs time of the day

Attack emulation

One python scripts can be found in /source_code/attack_emulation folder for generating DDoS attack on the original dataset.

generate_attack.py

This script genrates the attacked dataset by considering the ratio of the nodes that are under attack, the attack duration, and also the attack start dates.

Input:

• Bening dataset
• Number of attack days
• Attack ratio
• Attack duration
• Attack start dates

Output:

• Attacked dataset

Training neural network

Three python scripts can be found in /source_code/nn_training folder for generating the labeled training and testing dataset, train a feed-forward neural network, and generating the results of training.

generate_training_data.py

This script generates the training data by considering the different time windows for averaging the occupancies on the nodes.

Input:

• Attacked dataset
• Averaging time windows

Output:

• Training dataset

train_nn.py

This script create a feed-forward neural network to train on the training dataset for detecting the attackers. The scrip save the final model and also the epochs logs and weights.

Input:

• Training dataset
• Number of epochs

Output:

• Trained neural network model with epochs' logs and weights

generate_results.py

This script provides analysis like, accuracy, loss, confusion matrix, etc. based on the trained model. Furthermore, it plots that true positive, false positive, and true attacks versus time.

Input:

• Training dataset
• Trained neural network model

Output:

• General analysis on the training like accuracy, loss, confusion matrix, etc.
• Plots of true positive, false positive, and true attacks versus time for different attack ratios and durations

Acknowledgement

This material is based upon work supported in part by Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001120C0160 for the Open, Programmable, Secure 5G (OPS-5G) program. Any views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.