Volume 11, Number 3

Classification Procedures for Intrusion Detection Based on KDD CUP 99 Data Set


Shaker El-Sappagh, Ahmed Saad Mohammed and Tarek Ahmed AlSheshtawy, Benha University, Egypt


In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks


Intrusion Detection, Data Mining, KDD CUP 99, False Alarms