Mirai-based multi-class dataset

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Abstract 

The dataset used in the study consists of different IoT network traffic data files each IoT traffic data has files containing benign, i.e. normal network traffic data, and malicious traffic data related to the most common IoT botnet attacks which are known as the Mirai botnet. From these different types of IoT botnet attacks, we focused on SYN-Flooding, ACK-Flooding, and HTTP-Flooding and the normal/benign traffic data for the study. The dataset used in the study was extracted and adapted from IoT network intrusion dataset and developed in our previous work for binary attack classification, and provides an opportunity to model multi-class classification besides classifying a sample as benign, ACK-Flooding, SYN-Flooding, and HTTP-Flooding attack. There are 3 types of Mirai-based botnet attacks that can be detected using network traffic data during the study. The newly generated dataset was extracted from the original dataset which was in a PCAP format to CSV with a total of 16 features. The detailed features of the newly generated dataset for multi-class attack classification used in this study would be discussed in the experiment result analysis and discussion section of the study.

Instructions: 

The dataset is prepared in a suitable way for applying machine learning models for attack classiffication

Comments

ok

Submitted by shahid Mahmood on Fri, 03/31/2023 - 02:47

thanks

Submitted by Muhammet Sinan ... on Wed, 03/20/2024 - 12:37