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Research Article | Open Access
Volume 15 2023 | None
HONEYPOT WITH MACHINE LEARNING BASED DETECTION FRAMEWORK FOR DEFENDING IoT BASED BOTNET DDoS ATTACKS
Mr.G.ANIL KUMAR, BANDARU GOVARDHAN, MOHD KHAJA MOINUDDIN, PEDDINTI ROHITH, SAI VINEET KUMAR
Pages: 192-196
Abstract
This project proposes a honeypot with machine learning based detection framework to defend against IoT based botnet DDoS attacks. The honeypot server acts as a decoy between the centralized server and the IoT network, and uses machine learning algorithms such as SVM, KNN, Random Forest, Decision Tree and Neural Network to classify the requests as normal or malicious. The honeypot server also extracts information from the attackers and informs the centralized server and the IoT network to block them. The paper claims that this approach can solve zero-day DDoS attacks and outperforms the existing signature-based detection methods. The paper uses real IoT data to train and test the machine learning models, and reports that SVM, KNN and Neural Network achieve the best performance.
Keywords
This project proposes a honeypot with machine learning based detection framework to defend against IoT based botnet DDoS attacks.
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