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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: DEStech Transactions on Computer Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
