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A considerable portion of the machine learning literature applied tointrusion detection uses outdated data sets based on a simulated network with alimited environment. Moreover, flaws usually appear in datasets and the way wehandle them may impact on measurements. Finally, the detection capacity ofintrusion detection is highly influenced by the system configuration. We focuson a topic rarely investigated: the characterization of anomalies in a largenetwork environment. Intrusion Detection System (IDS) are used to detectexploits or other attacks that raise alarms. These anomalous events usuallyreceive less attention than attack alarms, causing them to be frequentlyoverlooked by security administrators. However, the observation of thisactivity contributes to understand the traffic network characteristics. On onehand, abnormal behaviors may be legitimate, e.g., misinterpreted protocols ormalfunctioning network equipment, but on the other hand an attacker mayintentionally craft packets to introduce anomalies to evade monitoring systems.Anomalies found in operational network environments may indicate cases ofevasion attacks, application bugs, and a wide variety of factors that highlyinfluence intrusion detection performance. This study explores the nature ofanomalies found in U-Tokyo Network using cooperatively Bro and Snort IDS amongother resources. We analyze 6.5 TB of compressed binary tcpdump datarepresenting 12 hours of network traffic. Our major contributions can besummarized in: 1) reporting the anomalies observed in real, up-to-date trafficfrom a large academic network environment, and documenting problems in researchthat may lead to wrong results due to misinterpretations of data ormisconfigurations in software; 2) assessing the quality of data by analyzingthe potential and the real problems in the capture process.

Analysis of Anomalies in the Internet Traffic Observed at the Campus Network Gateway