Automated Approach for DDOS Attacks Detection Based on Naive Bayes Multinomial Classifier

Abstract

Now days use of internet for the getting and sharing of knowledge is very common. The end users those are accessing internet or system are vulnerable to malicious user attacks which results into legitimate user prevented from accessing the websites. Recently there are several of methods presented for application layer DDoS attacks by considering the different properties of attacks. However most of methods are suffered from the poor accuracy performance of DDoS attack detection at application layer. Hence DDoS attacks has been low volume & act own as a legitimate transaction on layer seven means application layer hence such attacks are not detected easily by IDS (Intrusion Detection Systems) or firewall systems. We believe that, the accuracy and efficiency of attacks detection is based on correctness of capture data traffic. In state-of-art methods, there is no provision to remove the noisy data from the capture logs and hence leads to incorrect detection results. In this paper we presented the real time computer networks data capturing for normal as well as attack infected traffics, then design the preprocessing algorithm to remove irrelevant data to optimize the attack detection performance which helps to naive bayes algorithm to classification. In this architecture, we use LOIC doss attack generator tool to create attack at packet capturing time of communication network. The experimental results show that proposed data pre-processing method with naive bayes multinomial is easy and efficient as compared to state-of-art solutions. To classify normal packets and DDoS attack we use naive bayes multinomial classifier