Explanation-based learning to recognize network malfunctions

Several network troubles and/or malfunctions may occur due to the heavy traffic of recent computer networks. The discovering of some types of these troubles is not straightforward. Therefore, there is a real need to an intelligent system to recognize that type of problems using a priori background knowledge.

The aim of this work is to present a network-monitoring utility that can discover various operational patterns and can provide sensible advice that may support the network administrator. It presents a machine learning system that can recognize network malfunctions. Such recognition process may be expressed in structured patterns to support network administrator for both problem solving and network management.

To achieve this objective an explanation_based learning (EBL) procedure is used to obtain operational rules. In this case, the domain (network) knowledge is formally expressed and only one training example is analyzed in terms of this knowledge. This system uses a relational database to store and maintain the knowledge_base.

The main contribution of the proposed system is to discover the abnormal patterns (malfunctions) of the network traffic. These abnormal patterns, as such, could be recognized from a real network using EBL. If the network administrator is advised with that malfunctions then he can adapt the current configuration in order to avoid the corresponding problems.