• Hari Krishna Deevi
• Dr.B,Bhanu Prakesh

With the advent of the Internet, users have access to a broad variety of distant services in the distributed computing environment. However, there are a number of security concerns that threaten the integrity of data transmission on the distributed computing platform. For instance, harmful code is just one facet of the Internet security danger posed by the botnet issue. Distributed denial of service (DDoS) assaults, click fraud, phishing, virus distribution, spam emails, and machine construction for illegal information/material exchange are all enabled by the botnet phenomena. Consequently, it is crucial to provide a reliable system for enhancing botnet identification, analysis, and eradication. In this study, we offer a technique for quickly and accurately pinpointing a handful of potentially malicious sites that are almost certainly bots. For more precise and detailed botnet identification, their communications can be sent to DPI-based systems. Our technique drastically reduces the amount of network traffic subject to DPI by employing a unique adaptive packet sampling algorithm and a scalable spatial-temporal flow correlation approach, hence increasing the scalability of existing botnet detection systems.

Internet, Botnet, Sampling, Detection, Network security

"Determine Efficient and Effective Botnet Detection with Adaptive Traffic Sampling", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.358-362, October-2022, Available :http://www.jetir.org/papers/JETIRFU06044.pdf

"Determine Efficient and Effective Botnet Detection with Adaptive Traffic Sampling", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. pp358-362, October-2022, Available at : http://www.jetir.org/papers/JETIRFU06044.pdf