Optimizing an Intrusion Tolerant Database System Using Neural Network
Traditional database security mechanisms focus on either protection or prevention. However, these mechanisms have not any strategy in the presence of successful attacks. To solve this problem, the Intrusion Tolerant Database System (ITDB) was introduced. ITDB uses the new generation of database security mechanisms to guarantee specified levels of data availability, integrity and confidentiality in the presence of successful attacks. These mechanisms include Attack Isolation and Multiphase Damage Confinement. In this paper, the authors will present a practical model to utilize the combination of intrusion tolerance techniques for managing the ITDB architecture. Using this practical model, they will be able to secure the system's required integrity and availability levels considering the changes in the environment.