Lin, Hua.

We discuss a set of indirect combining techniques for addressing multi-category classification problems that have been used in many domains, but not for intrusion detection systems. In contrast to the indirect combining techniques, direct techniques generally extend associated binary classifiers to handle multi-category classification problems. An indirect combining technique decomposes the original multi-category problem into, based on some criteria, multiple binary-category problems. We investigated two different approaches for building the binary classifiers. The results of the binary classifiers are then merged using a combining technique---three different combining techniques were studied. We implement some of the indirect combining techniques proposed in recent literature, and apply them to a case study of the DARPA KDD-1999 network intrusion detection project. The results demonstrate the usefulness of using indirect combining techniques for the multi-category classification problem of intrusion detection systems.