GENETIC ALGORITHM BASED GENERATION OF CLASSIFICATION RULES FOR NETWORK INTRUSION DETECTION
Author’s Name : Dr N Arumugam
Volume 02 Issue 01 Year 2015 ISSN No: 2349-252X Page no: 16-19
Abstract – In recent days, due to the rapid expansion of Internet, computer systems are facing vast number of security threats. In spite of numerous detection and defense methodologies proposed for information assurance, it is still very difficult to protect computer systems. As a result, unwanted intrusions take place when the actual software systems are running. Recently soft computing based intrusion Detection systems (IDs) have been subjected to extensive researches because they can detect both misuse and anomaly detection. In this paper the method of learning the Intrusion Detection, rules based on genetic algorithms was presented. The genetic algorithm is employed to derive a set of classification rules from network audit data, and the support-confidence framework is utilized as fitness function to judge the quality of each rule. The generated rules are then used to detect or classify network intrusions in a real-time environment. The proposed representation of rules and the effective fitness function is easier to implement while providing the flexibility to either generally detect network intrusions or precisely classify the types of attacks. Experiments results shows, the characters of an attack such as SMURF and SNMP get attack were summarized through the Modified and corrected KDD 99 data set and the effectiveness and robustness of the approach are proved.
Keywords – Intrusion Detection, Genetic Algorithm, KDD Cup Data Set
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