KNN Intrusion Detection Model Based on Meta-optimization

To improve the performance of intrusion detection model based on KNN, a KNN intrusion detection model using meta-optimization based on a local search algorithm for feature weighting was proposed. The differential evolution algorithm was used to optimize feature weights and the LUS based meta-optimization was selected to optimize the differential evolution. The NSL dataset was used to carry out the experiments. The proposed model was compared with that optimized by other commonly used heuristic algorithms, including GA, PSO and GWO. Results show that compared with the traditional KNN, the accuracy of the proposed method is improved by 2.86%, the detection rate increased by 3.18% and the false positive rate is reduced by 50%. The optimization based on meta-optimization is better than other optimization algorithms commonly used.