Intrusion detection is an important role in implementing and enforcing an organizational security policy. As information resources grow in complexity, effective security systems must evolve. With the propagation of the number of vulnerability points introduced by the use of distributed systems, some type of assurance is needed that the systems and network are secure. Here, we propose novel optimistic approach, Bootstrapped Optimistic Decision Tree Construction (BOAT) for decision tree construction that improves upon earlier algorithms in both performance and functionality. It is the first scalable algorithm with the ability to incrementally update the tree with respect to both insertions and deletions over the dataset. It allows updating the tree dynamically. This property is valuable in dynamic environments such as data warehouses, in which the training dataset change over time. The performance evaluation of that BOAT algorithm demonstrates its scalability, incremental processing of updates, and speedup processing to identify intruders over existing algorithms, &reduces the number of database scans, and also provides flexibility.