Improving Discrimination Accuracy Rate Of DDoS Attacks and Flash Events

Autor:	Sahareesh Agha, Osama M. Hussain Rehman
Rok vydání:	2020
Předmět:	Web server Computer science business.industry Denial-of-service attack Feature selection Internet security Computer security computer.software_genre Random forest Statistical classification Flash (photography) The Internet business computer
Zdroj:	2020 International Conference on Cyber Warfare and Security (ICCWS).
Popis:	Millions of people across the world are using internet for their day to day activities. People are highly dependent on internet as they are using internet resources for their work in every field. It connects billions of people across the world. Internet Security has become a big issue and with passage of time. Among many threats, the Distributed Denial-of-Service (DDoS) attack is the most frequent threats in the networks. Consequences of these attacks are more powerful when launched during flash events which are legitimate traffic and cause denial of service. This paper focuses on improving discrimination accuracy rate of DDoS Attacks and Flash events. Random forest is used for classification. Symmetric uncertainty is used for feature selection. NSL KDD data set used to evaluate performance of classifier. Weka is used for implementing algorithms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::99ba465f8b76302e9b85db96c5972b89 https://doi.org/10.1109/iccws48432.2020.9292377 Zobrazit plný text záznamu