The main objective of this research is to achieve precise and accurate results through the utilization of the OCL algorithm. In comparison, the results obtained from the OCL approach will be contrasted with those derived from the recently developed NB algorithm, aiming to enhance the overall effectiveness of fraud detection. Materials and Methods: A 95% confidence interval and a G-power value of 0.8 were included in the investigation. For the predictive safety study utilizing the Novel NB technology, a sample size of 20 individuals was employed. Simultaneously, another 20-person sample was utilized for the purpose of comparing outcomes through the OCL methodology. The NB demonstrated superior performance, achieving an accuracy of 89.34 per cent, surpassing the 81.61 per cent accuracy achieved by the OCL. Conclusion: In conclusion, by evaluating the performance of NIDS using Naïve Bayes algorithm and one-class learning on a dataset of network traffic data, it is possible.