IoT DoS and DDoS Attack Dataset

Citation Author(s):
Faisal
Hussain
University of Engineering and Technology (UET) Lahore, Pakistan
Syed Ghazanfar
Abbas
University of Engineering and Technology (UET) Lahore, Pakistan
Muhammad
Husnain
University of Engineering and Technology (UET) Lahore, Pakistan
Ubaid U.
Fayyaz
University of Engineering and Technology (UET) Lahore, Pakistan
Farrukh
Shahzad
University of Engineering and Technology (UET) Lahore, Pakistan
Ghalib A.
Shah
University of Engineering and Technology (UET) Lahore, Pakistan
Submitted by:
Faisal Hussain
Last updated:
Mon, 08/16/2021 - 02:05
DOI:
10.21227/0s0p-s959
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Abstract 

The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules. However, these solutions can become reliable and effective when integrated with artificial intelligence (AI) based techniques. During the last few years, deep learning models especially convolutional neural networks achieved high significance due to their outstanding performance in the image processing field. The potential of these convolutional neural network (CNN) models can be used to efficiently detect the complex DoS and DDoS by converting the network traffic dataset into images. Therefore, in this work, we proposed a methodology to convert the network traffic data into image form and trained a state-of-the-art CNN model, i.e., ResNet over the converted data. The proposed methodology accomplished 99.99% accuracy for detecting the DoS and DDoS in case of binary classification. Furthermore, the proposed methodology achieved 87% average precision for recognizing eleven types of DoS and DDoS attack patterns which is 9% higher as compared to the state-of-the-art.

Instructions: 

- The Dataset is created by applying data pre-processing techniques over CIC-DDoS2019 Dataset avaialable at: https://www.unb.ca/cic/datasets/ddos-2019.html

- The original dataset was in .csv format. However, to efficiently utilize the potential of CNNs for the DoS and DDoS attacks detection, the network traffic data is converted into image form.

The following paper provides the details of how did convert the network traffic data into image form and trained a state-of-the-art CNN model, i.e., ResNet over the converted data?

Paper: IoT DoS and DDoS Attack Detection using ResNet

Authors: Faisal Hussain, Syed Ghazanfar Abbas, Muhammad Husnain, Ubaid U. Fayyaz, Farrukh Shahzad, Ghalib A.Shah

Publisher: IEEE

Online at: https://ieeexplore.ieee.org/abstract/document/9318216/

 

BibTex:

@inproceedings{hussain2020iot,

  title={IoT DoS and DDoS Attack Detection using ResNet},

  author={Hussain, Faisal and Abbas, Syed Ghazanfar and Husnain, Muhammad and Fayyaz, Ubaid U and Shahzad, Farrukh and Shah, Ghalib A},

  booktitle={2020 IEEE 23rd International Multitopic Conference (INMIC)},

  pages={1--6},

  year={2020},

  organization={IEEE}

}