Effective management of computer networks has become a more and more difficult job because of the rapid development of the network systems. Fault identification is to find where is the problem of the network and what is it. Data mining generally refers to the process of extracting models from large stores of data. We can use data mining techniques to help us in the fault identification task. Existing approaches of fault identification are introduced and a new approach of fault identification is proposed. This approach improves MSDD algorithm but it need more computation. So some new techniques are used to increase the efficiency.
In a modern computer system, intrusion detection has become an essential and critical component. Data mining generally refers to the process of extracting models from large stores of data. The intrusion detection system first apply data mining programs to audit data to compute frequent patterns, extract features, and then use classification algorithms to compute detection models. The most important step of this process is to determine relations between fields in the database records to construct features. The standard association rules have not enough expressiveness. Intrusion detection system can extract the association rule with negations and with varying support thresholds to get better performance rather than extract the standard association rule.