This is a fairly difficult data set to come by since

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active edges. This is a fairly difficult data set to come by, since collection is a chal- lenge for many reasons. At many universities, for example, data from per- sonal machines is not collected for privacy reasons. In addition, sensors in the network that measure this data need to be distributed well to have access to all of the connections, and when they are, the data rate can be very high, so that significant engineering is required to collect and store these measurements. For these reasons, data of this type is not widely available, and there is little published work using it. The LANL has invested sig- nificant resources in collection over the past ten years, and since attacks can and are observed via edge data, we feel it is an extremely fruitful data stream for researchers to collect and analyze. A plot of one of these edges is given in Figure 3.1. There is an enormous variety among the edges in a typical network. Those that are driven by human presence, such as the edge created between a desktop and an email server when a user checks his email, tend to be similar to Figure 3.1. Others, such as edges between load-balancing servers, tend to be much smoother. Several of the edges exhibit periodic behavior, at multiple timescales. Copyright © 2014. Imperial College Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/16/2016 3:37 AM via CGC-GROUP OF COLLEGES (GHARUAN) AN: 779681 ; Heard, Nicholas, Adams, Niall M..; Data Analysis for Network Cyber-security Account: ns224671
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Statistical Detection of Intruders Within Computer Networks 73 Fig. 3.1. Counts of connections per minute between two machines in LANL’s internal network. The connections originate at a specific user’s machine, and the destination is a server providing virtual desktop services. In order to establish a baseline of behavior for each edge, modeling is used. Section 3.4 presents three models, one of which, the hidden Markov model, attempts to capture this human behavior, by explicitly accounting for the burstiness apparent on this edge. A more thorough modeling effort is required to accurately reflect the variety of edges seen in this data, an effort that is ongoing, but the three models presented represent a first step. Attacks create deviations not just on single edges, but across multiple connected edges, a subject discussed in the next section. Under an inde- pendence assumption among the edges in the subgraph, models for edges lead to models for subgraphs as discussed in Section 3.3. 3.1.2. Example traversal attack A common initial stage of attack on computer networks is to infect a machine on the network using malicious software. One method for initial infection is known as a phishing attack, where an email that includes a link to a malicious website is sent to a set of users on a network. When the user clicks on the link, their machine becomes infected, giving the attacker some form of access to the machine.
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