Aiming at the shortcomings in intrusion detection systems (IDSs) used in commercial and research fields,we propose the MA-IDS system, a distributed intrusion detection system based on data mining. In this model, misuse intrusion detection system (MIDS) and anomaly intrusion detection system (AIDS) are combined. Data mining is applied to raise detection performance, and distributed mechanism is employed to increase the scalability and efficiency. Host- and network-based mining algorithms employ an improved Bayesian decision theorem that suits for real security environment to minimize the risks incurred by false decisions. We describe the overall architecture of the MA-IDS system, and discuss specific design and implementation issue.