Attack Detection and Analysis in Virtual Wireless Networks using Mininet-WiFi, CIC Flowmeter, Wireshark, and Machine Learning
Keywords:
Mininet Wi-fi, DDOS, CIC flowmeter, Wireshark, hping3, Classifiers, sklearn, PandasAbstract
Mininet-WiFi is a powerful tool for creating virtual wireless network environments to test various networking scenarios. However, attacks like Distributed Denial of Service (DDOS) attacks can still affect these simulated networks. A CIC flowmeter can be used to monitor network traffic and Wireshark can be used to record and analyse network data in real-time to detect and analyse such assaults. However, the data obtained from these tools may be noisy, which can negatively impact the accuracy of attack detection and analysis. To address this issue, machine learning algorithms can be applied to clean the data obtained from CIC flowmeter and Wireshark. In this study, we propose an approach that uses Mininet-WiFi in combination with a CIC flowmeter, Wireshark, and machine learning algorithms for data cleaning to detect and analyse DDOS attacks in a virtual wireless network. We will discuss the setup and configuration of Mininet-WiFi, CIC flowmeter, and Wireshark, and demonstrate how machine learning algorithms can be used to clean the data obtained from these tools to improve the accuracy of attack detection and analysis.
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