Detection of Distributed Denial of Service Attack using Random Forest Algorithm

The Distributed Denial of Service (DDoS) attack entails flooding an online service with traffic from multiple sources such that it is rendered unavailable. These attacks have been identified by many researchers using machine learning algorithms. In this paper, Ping of death attack is executed and th...

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Bibliographic Details
Published in:	2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) pp. 382 - 386
Main Authors:	C, Murukesh, B, Kishore Kannan, Kumar A, Thilak, B, Venkat, V, Haris Kumar
Format:	Conference Proceeding
Language:	English
Published:	IEEE 13-12-2022
Subjects:	Classification algorithms Cyber security Denial-of-service attack Distributed Denial of Service Machine learning algorithms Ping of Death Random Forest algorithm Renewable energy sources Security Software Software algorithms Splunk
Online Access:	Get full text
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Summary:	The Distributed Denial of Service (DDoS) attack entails flooding an online service with traffic from multiple sources such that it is rendered unavailable. These attacks have been identified by many researchers using machine learning algorithms. In this paper, Ping of death attack is executed and their detection was performed using random forest algorithms. A DDoS attack is detected by Splunk software, which collects attack details about the data packets. Data from Kaggles's dataset is used to train the machine learning algorithm. An algorithm based on the random forest is used to visually differentiate between the normal and attacked samples whose accuracy is equal to 99.8%. During the attack on the network, the Central Processing Unit and Wi-Fi performances are also analyzed.
DOI:	10.1109/ICACRS55517.2022.10029249