VFSA-C4.5 feature selection algorithm for network intrusion detection

The VFSA-C4.5 a new feature selection algorithm is proposed to detect network intrusions. The algorithm uses the very fast simulated annealing (VFSA) as the search strategy to specify a candidate subset for evaluation, and then uses the decision tree of C4.5 as the evaluation function to obtain the optimum feature subset for intrusion detection by the data classification error rate. The feasibility of the feature selection algorithm was examined by conducting several experiments on the KDD 1999 intrusion detection dataset. The experimental results show that the VFSA-C4.5 algorithm has higher detection rate and lower false alarm rate compared with other feature selection algorithms for network intrusion detection. Furthermore, the proposed algorithm can reduce computational resources of intrusion detection, improve the detection speed and is more suitable for the real network applications than the traditional ones.