Statistical, real-time classification of IP traffic in Linux operating system

The thesis introduces a practical system for statistical classification of IP traffic. Two novel algorithms are applied and extended. They are based on feature vector classification using SVM. A software library written in C language is presented. Resultant system can monitor network interfaces in realtime and read off-line packet trace files. Simultaneous classification, system training, and performance evaluation is possible. The system yields very good results, in terms of quality and packet processing speed, achieving %TP>97 and %FP=0 on average.