CLOUD ATTACK DATASET

Citation Author(s):
Laxmi Pranitha
Rachamalla
VNR Vignana Jyothi Institute of Engineering and Technology
Anusha
Akkidasari
VNR Vignana Jyothi Institute of Engineering and Technology
Sandhya
Madiga
VNR Vignana Jyothi Institute of Engineering and Technology
Harshitha
Mittapalli
VNR Vignana Jyothi Institute of Engineering and Technology
Radhakrishna
Vangipuram
VNR Vignana Jyothi Institute of Engineering and Technology
Submitted by:
Laxmi Pranitha ...
Last updated:
Tue, 11/30/2021 - 10:18
DOI:
10.21227/05ep-zk84
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Abstract 

With the modern day technological advancements and the evolution of Industry 4.0, it is very important to make sure that the problem of Intrusion detection in Cloud , IoT and other modern networking environments is addressed as an immediate concern. It is a fact that Cloud and Cyber Physical Systems are the basis for Industry 4.0. Thus, intrusion detection in cyber physical systems plays a crucial role in Industry 4.0. Here, we provide the an intrusion detection dataset for performance evaluation of machine learning and deep learning based intrusion detection systems. For this, we have considered, the CIC-IDS 2017 dataset available publicly from Canadian Institute of Cybersecurity. A total of 100541 traffic instances are considered which belong to one of the 14 traffic classes namely bening and thirteen attack classes. All these network instances are converted into traffic images each of 9x9 pixel size. Researchers working on machine learning and deep learning areas can utilize this dataset for their experimental analysis.

Instructions: 

 

- This Dataset is created by applying data pre-processing techniques over CIC-IDS2017 Dataset avaialable at: https://www.unb.ca/cic/datasets/ids-2017.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 with each image of 9x9 pixel size.

Comments

Thanks.

Submitted by Gianetan Sekhon on Tue, 11/30/2021 - 09:00