Intrusion Detection of Data Platform Based on Extreme Learning Machine in Civil and Military Integration

Abstract

With the deepening development of military and civilian integration, the explosive growth of internal information is guaranteed and the advantages of big data in ensuring system efficiency are increasingly obvious. However, many hidden dangers are exposed. To solve the problem of ELM, which is overly dependent on the single hidden layer feedforward neural network (SLFN) with many hidden nodes, this paper proposes an SVM-based ELM (SVM-ELM) Intrusion detection algorithm. This algorithm reduces the number of hidden layer nodes significantly and uses SVM weight to optimize the weights and offsets of each node, which improves the decision-making level of nodes and significantly improves the decision performance of ELM. Verification using KDD99 dataset shows that the generalized performance of SLFN with only 5 nodes constructed by SVM-ELM has obvious advantages compared with the original ELM. Compared with other algorithms such as BP, the SVM-ELM algorithm can quickly complete the training and has a higher detection accuracy.