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Design and Implementation of a System for Denial of Service Attack Detection Based on Multivariate Correlation Analysis

Priti G. Harne , Prof.V.M.Deshmukh

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, present a DoS attack detection system that uses Multivariate Correlation Analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. the application of Multivariate Correlation Analysis(MCA) where SVM(Support Vector Machine) is used to Train the data & it classify the data/attack. Clustering is use for the alert aggregation process. Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis is a generative modelling approach using probabilistic methods.MCA-based DoS attack detection system employs the principle of anomaly detection and misuse based detection in attack recognition. This makes, solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only & also detecting various types of viruses. this system also checking for conditional privileges like packet level intruder, process level intruder etc.Furthermore, The effectiveness of propose detection system is evaluated using KDD Cup dataset