Improvising DoS Attack Detection Using Multivariate Correlation Analysis
|Archana K. Salaskar, Bharti Kale
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Denial of service (DoS) attack is potential damaging attack which degrades the performance of online servers within seconds. This attack imposes intensive computation on the target server by flooding it with large useless packets. The target server can be forced out of service from a few minutes to even several days. This causes work down of crucial business services running on the target victim. To cope with such damaging attacks becomes challenge for the researchers. Solution for this attack mainly focuses on the development of network-based detection mechanisms. Detection systems based on these mechanisms monitor traffic transmitting over the protected networks. The proposed system monitors the network traffic to extract the features which are directly associated with DoS attacks. Based on these features, the multivariate correlation model generates geometrical triangular area measurements for normal profiles. These models are used as reference to detect any unknown DoS attack in the network. But alone MCA based system may not be accurate for attack detection. The innovative work imposes behavioral model integrated with MCA to enhance the accuracy of DoS attack detection.