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.
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Singh, K.J., De, T. (2015). An Approach of DDOS Attack Detection Using Classifiers. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2550-8_41
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DOI: https://doi.org/10.1007/978-81-322-2550-8_41
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