Deep Learning-Powered Intrusion Detection Systems: Enhancing Efficiency in Network Security

Although deep learning models have shown impressive results in various fields, their intrinsic complexity sometimes makes them difficult to understand, raising questions about their dependability and credibility in crucial applications. The usefulness of LRP in improving the interpretability of deep learning models is examined in the suggested work. Decision-making process insights are obtained using LRP to ascribe the significance of input characteristics to the model’s predictions. The investigation showed that LRP greatly enhances DL models’ interpretability. Users may discover key characteristics and get essential insights into the model’s decision-making process by viewing the relevance scores provided to input features. With a final interpretability score of 95%, the suggested strategy significantly improved over current techniques. The results show how crucial interpretability is for DL models and emphasize how helpful LRP is in boosting transparency and reliability. LRP helps users comprehend and evaluate the choices made by sophisticated neural networks by offering explanations for model predictions. This helps to improve the dependability and practicality of deep learning in real-world contexts.