Detection of Intrusions in KDDCup Dataset using GA by Enumeration Technique
In the last decades, there has been a massive growth in network connectivity between computer system which has achieved boundless potential outcomes and opportunities. Sadly, security related issues have likewise expanded at the same rate. Computer systems become victims of such attacks. These attacks or intrusions are modified information that cause harm to the working system program or application programs, typically read or alter private data or render the system futile. Several techniques are used to prevent and detect such attacks or intrusions. This paper presents an efficient GA based methodology to produce the classification rules for Network intrusion detection system. The chromosome structure has been selected by applying enumeration technique in which the computational time required to produce the population is significantly reduced and near optimal rules are generated. These classification rules are used to find networking attacks or intrusions. The proposed system is applied on KDDCup99 Dataset to yield more efficient and effective classification rules.