An Approach of DDOS Attack Detection Using Classifiers View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Khundrakpam Johnson Singh , Tanmay De

ABSTRACT

To defend and protect web server from the attack, it is important to know the nature and the behaviour of legitimate and illegitimate clients. It is also important to provide access to the legitimate clients and provide a defence system against illegitimate clients. The Distributed Denial of Service (DDoS) attack is a critical threat to the Internet. By using its application layer protocol DDoS can cause a massive destruction by silently making an entrance to the web server as it act as one of the legitimate clients. The paper uses parameter of the network packet like http GET, POST request and delta time to compute the accuracy in finding out the possible attack. We use different classifiers like Naive Bayes, Naive Bayes Multinomial, Multilayer Perception, RBF network, Random Forest etc. to classify the attack generated dataset. We compare the accuracy, true positive rate, false positive rate of each algorithm by finding the confusion matrix. More... »

PAGES

429-437

Book

TITLE

Emerging Research in Computing, Information, Communication and Applications

ISBN

978-81-322-2549-2
978-81-322-2550-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-81-322-2550-8_41

DOI

http://dx.doi.org/10.1007/978-81-322-2550-8_41

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037743296


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