Optimizer Algorithms Analysis for Intrusion Detection System on Deep Neural Network

Intrusion Detection Systems (IDS) play a critical role in identifying potential threats and intrusions in real-time within information technology infrastructures. The development of IDS using Deep Neural Networks (DNN) with the UNSW-NB15 dataset has shown significant potential in improving attack classification accuracy. However, the performance of the DNN-based IDS models is highly dependent on the choice of optimization algorithm. This study compares the performance of several commonly used optimizers in DNN training, including SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, Adafactor, and Nadam. The quantitative analysis demonstrates that Adam achieves the highest accuracy among all optimizers tested, while Adadelta performs the worst. RMSprop shows instability in both validation accuracy and loss convergence, indicating challenges in adapting the learning rate for consistent learning. The ANOVA analysis yields an F-statistic of 34.687, which is greater than the F-critical value of 2.140 at a significance level of α = 0.05. This result confirms a statistically significant difference in performance among the tested optimization algorithms. These findings provide valuable insights for selecting the most appropriate optimizer to enhance the performance of DNN-based intrusion detection systems. Furthermore, this research contributes to the existing literature by offering a comprehensive comparative evaluation of optimizers, supporting future studies in improving IDS optimization strategies.