Network Intrusion Detection Via Pair wise Angular Distance Computation Supported By Genetic Algorithm

摘要: The tremendous growth of internet supported by the extensive connectivity among systems within different networks has promoted execution class unauthorized activities and made their detection sophisticated. Preventing such unlawful acts are rarely possible hence detecting them is an essential for ensuring security information systems. paper presents a network intrusion via pair wise angular distance computation genetic algorithm. NSLKDD dataset been used training testing this supervised learning method. gain attribute selection operation on dataset. proposed methodology expressing lower time space complexity.