Network packet traces, despite having a lot of noise, contain priceless information, especially for investigating security incidents or troubleshooting performance problems. However, given the gigabytes of flow crossing a typical medium sized enterprise network every day, spotting malicious activity and analyzing trends in network behavior becomes a tedious task. Further, computational mechanisms for analyzing such data usually take substantial time to reach interesting patterns and often mislead the analyst into reaching false positives, benign traffic being identified as malicious, or false negatives, where malicious activity goes undetected. Therefore, the appropriate representation of network traffic data to the human user has been an issue of concern recently. Much of the focus, however, has been on visualizing TCP traffic alone while adapting visualization techniques for the data fields that are relevant to this protocol's traffic, rather than on the multivariate nature of network security data in general, and the fact that forensic analysis, in order to be fast and effective, has to take into consideration different parameters for each protocol. In this thesis, we bring together two powerful tools from different areas of application: SiLK (System for Internet-Level Knowledge), for command-based network trace analysis; and ComVis, a generic information visualization tool. We integrate the power of both tools by aiding simplified interaction between them, using a simple GUI, for the purpose of visualizing network traces, characterizing interesting patterns, and fingerprinting related activity. To obtain realistic results, we applied the visualizations on anonymized packet traces from Lawrence Berkley National Laboratory, captured on selected hours across three months. We used a sliding window approach in visually examining traces for two transport-layer protocols: ICMP and UDP. The main contribution of this research is a protocol-specific framework of visualization for ICMP and UDP data. We explored relevant header fields and the visualizations that worked best for each of the two protocols separately. The resulting views led us to a number of guidelines that can be vital in the creation of "smart books" describing best practices in using visualization and interaction techniques to maintain network security; while creating visual fingerprints which were found unique for individual types of scanning activity. Our visualizations use a multiple-views approach that incorporates the power of two-dimensional scatter plots, histograms, parallel coordinates, and dynamic queries. / Master of Science