@article{MAKHILLIJSC20127521090,
    title = {Optimizing an Intrusion Tolerant Database System Using Neural Network},
    journal = {International Journal of Soft Computing},
    volume = {7},
    number = {5},
    pages = {224-234},
    year = {2012},
    issn = {1816-9503},
    doi = {ijscomp.2012.224.234},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2012.224.234},
    author = {Z.,M.,M. and},
    keywords = {intrusion detection,database security,Intrusion tolerance,attack isolation,damage confinement,neural network},
    abstract = {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 study, researchers 
  will present a practical model to utilize the combination of intrusion tolerance 
  techniques for managing the ITDB architecture. Using this practical model, researchers 
  will be able to secure the system’s 
  required integrity and availability levels considering the changes in the environment. 
  Researchers will also introduce an intelligent method for determining the significance 
  degrees of data objects in the optimized attack isolation technique.}
    }