**7.3 Hybrid-based methodology**


### *Updates on Software Usability DOI: http://dx.doi.org/10.5772/intechopen.107423*

was developed using a C5.0 decision tree classifier. The next section was developed with OC-SVM for irregularity discovery. The NSL-KDD and Australian Defense Force Academy (ADFA) datasets were used by the experts to demonstrate the model, and the results revealed that the half-and-half model performed better than single-based models.


identify persistent VANET disruptions. In comparison with other models, this maintained a better presentation in terms of accuracy, computational efficiency, and identification rate.

