Data dependency based logging for defensive information warfare

Recovery from information attacks is a difficult task as database management systems are not designed to deal with malicious committed transactions, Few existing methods developed for this purpose heavily rely on logs and require that the log must not be purged. This causes the log grow tremendously and, therefore, making recovery a prolonged process since scanning the log takes enormous amount o f time. Hence, it is necessary to identi~ and skip parts o f logs that contain unaffected operations. In this research, we have used data dependency approach to divide a log into multiple segments, each segment containing only related operations. We have presented the model and the algorithm for log clustering which will significantly enhance the performance of database recovery for defensive information warfare.