The experiment shows that classifiers are good predictors when combined with our selected Netflow characteristics and feature selection techniques.ĭuring the COVID-19 epidemic, the online prescription pattern of Internet healthcare provides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. The machine learners effectively identify multiple slow DoS attacks, according to our findings. We examine ways to enhance machine learning techniques for detecting slow application-layer DoS attacks that employ these methodologies. We perform a feature extraction technique, Principal Component Analysis (PCA), on the combined dataset to reduce dimensionality. We perform a feature-scaling technique that applies a normalization filter to the combined dataset. We combine four application-layer DoS attack datasets: Slow Read, HTTP POST, Slowloris, and Apache Range Header. Our experiment proposes a generalized detection approach to identify features for application-layer DoS attacks that is not specific to a single slow DoS attack. They target weaknesses in HTTP, the most extensively used application-layer protocol on the Internet. Flood and “low and slow” attacks are examples of application-layer attacks. It is common for application-layer attacks to concentrate on these protocols because attackers can exploit some weaknesses. Hypertext Transfer Protocol (HTTP), Domain Name Service (DNS), Simple Mail Transfer Protocol (SMTP), and other application protocols have had increased attacks over the past several years. Due to these challenges, network security has become increasingly more challenging to ensure. One of these techniques is application-layer DoS. With the massive resources and strategies accessible to attackers, countering Denial of Service (DoS) attacks is getting increasingly difficult.

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