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Network traffic anomaly detection can find unusual events cause by hacker activity. Most research in this area focus on supervised and unsupervised model. In this work, we proposed a semi-supervised model based on combination of Mahalanobis distance and principal component analysis for network traffic anomaly detection. We also experiment clustering technique with suitable features to remove noise...
Network traffic anomaly detection can help to early detect network attacks because hacker's activities may result in unusual changes of network traffic, that are significant fluctuations compared to normal traffic of the network Among various anomaly detection approaches, principal component analysis (PCA) has been seen as an effective solution. Until now, PCA is basically applied to dimension reduction...
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