Network Intrusion Detection Via Pair wise Angular Distance Computation Supported By Genetic Algorithm
C. Geetha Priya, Mintu Philip · 2014
The tremendous growth of internet supported by the extensive connectivity among systems within different networks has promoted the execution of class of unauthorized activities and has made their detection sophisticated. Preventing such unlawful acts are rarely possible and hence detecting them is an essential for ensuring the security of information systems. The paper presents a network intrusion detection via pair wise angular distance computation supported by genetic algorithm. The NSLKDD dataset is been used for training and testing of this supervised learning method. The information gain is used for attribute selection operation on NSLKDD dataset. The proposed methodology is expressing lower time and space complexity.