Examining Committee:

Anomaly detection in computer networks yields valuable information on events relating to the components of a network, their states, the users in a network and their activities. This thesis provides a unified distribution-based methodology for online detection of anoma-lies in network traffic streams. The methodology is distribution-based in that it regards the traffic stream as a time series of distributions (histograms), and monitors metrics of distributions in the time series. The effectiveness of the methodology is demonstrated in three application scenarios. First, in 802.11 wireless traffic, we show the ability to detect certain classes of attacks using the methodology. Second, in information network update streams (specifically in Wikipedia) we show the ability to detect the activity of bots, flash events, and outages, as they occur. Third, in Voice over IP traffic streams, we show the ability to detect covert channels that exfiltrate confidential information out of the network. Our experiments show the high detection rate of the methodology when compared to other existing methods, while maintaining a low rate of false positives. Furthermore, we provide algorithmic results that enable efficient and scalable implementation of the above method