IMPLEMENTATION OF MACHINE LEARNING TECHNIQUES FOR CLOUD SECURITY IN DETECTION OF DDOS ATTACKS
Keywords:
Distributed Denial Of Service, DDoS, Cloud Computing, Security And Machine LearningAbstract
The term "cloud computing" refers to an approach of delivering software and hardware-based services over the internet. With the help of cloud computing, users can access their data and apps from any device Scalability, virtualization, access to user resources, lower infrastructure costs, and flexibility are the advantages that come with cloud computing. One of its downsides is that it is vulnerable to distributed denial of service attacks. Distributed denial of service attacks are carried out by several computer systems working together to target a certain resource, website, or server. The consequence is a denial of service for end users. False connection requests, an influx of messages, and twisted packets cause the system to slow down or even shut down. Real people and services can't get service because of this. This article delves into the topic of machine learning algorithms for detecting distributed denial of service (DDoS) attacks. This research includes two techniques and provides NSL-KDD datasets. On one hand, we have the Learning Vector Quantization (LVQ) filter; on the other, we have Principal Component Analysis (PCA), a dimensionality reduction approach. In order to detect distributed denial of service attacks (DDoS), the characteristics that were chosen from each approach were grouped using Decision Tree (DT), Naïve Bayes (NB), and Support Vector Machine (SVM). We compared the outcomes of various categorizations. When compared to other types of DT, LVQ-based DT is superior in identifying attacks.
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