Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning deep technologies. guide implementation trusted in IDS, this paper provides a comparison among models investigate based on accuracy regarding malicious data IDs. The four techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), naïve Bayes (NB). LSTM (one two layers) GRU layers). Two datasets used classify IDS attack type, including wireless sensor network (WSN-DS) KDD Cup dataset. A detailed eight techniques’ performance all features selected is made by measuring accuracy, precision, recall, F1-score. Considering findings related data, methodology, expert accountability, interpretability for solutions also becomes demanded enhance IDS.