An Intrusion Detection Algorithm Based on IFOA and WELM

doi:10.16182/j.issn1004731x.joss.19-0361

Abstract

Abstract: An intrusion detection algorithm of WELM optimized by IFOA is proposed. The advantages of short training time and good generalization performance of WELM are used, and the weight of minority attacks is increased, so that the recall rate of minority attacks in network attacks is greatly improved.The FOA with adaptive adjustment of the iterative step size is used, so the input weights and bias of the hidden layer in the WELM are globally optimized to avoid the algorithm falling into local optimal solution and realize the classification of the NSL-KDD intrusion detection data set. The experimental results show that the proposed algorithm improves the recall rate of minority attacks and the accuracy of the overall classification, and reduces the false positive rate.

Key words: intrusion detection, unbalanced data set, weighted extreme learning machine, fruit fly optimization algorithm

CLC Number:

TP391.9

Dang Jianwu, Tan Ling. An Intrusion Detection Algorithm Based on IFOA and WELM[J]. Journal of System Simulation, 2021, 33(2): 331-338.

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