Intrusion Detection Using a Boosting Ant-Colony-Based Data Miner

Data security plays an important role in the current networked computer systems. Because of lacking a distinctive boundary definition among normal and abnormal datasets, discriminating the normal and abnormal behaviors seems too much complex. This paper proposes a boosting Ant-colony-Based data miner for recognizing intrusion detection in computer networks. Extraction a classification rule set from a network dataset is the main purpose of the algorithm. These rules are capable of detecting normal and abnormal behaviors. The proposed algorithm is evaluated based on the detection, false alarm, and classification rates. Results show that the proposed boosting algorithm is capable of producing a reliable intrusion detection system.

کلیدواژه ها:

Ant-Colony ، intrusion detection ، data mining ، optimization algorithm ، local search algorithm