Generalization Performance of Internet of Things Intrusion Detection System Built on Impact-based Dataset Using TabNet Architecture

Internet of Things (IoT) networks consist of numerous small devices that are interconnected, gathering and transmitting data from one another to generate information on specific subject. Advancements of IoT have reached almost all aspects of modern life, ranging from simple room monitoring devices to industrial applications designed to streamline production and boost productivity. One subsequent factor of this rapid advancement is vulnerable devices placed on networks, creating a hole for malicious intrusions. One method in battling intrusions is Intrusion Detection Systems (IDS). This study presents a generalization analysis of a model created with TabNet trained on the CIC IoT-DIAD 2024 dataset. And for generalization, the model is tested against CIC IDS 2017, a popular dataset for IDS classification. The model was able to achieve decent results, achieving 84.39% and 91.80% F1-score for multiclass and binary classification, respectively. In contrast, only 50.29% and 21.26% F1-score was achieved for generalization for multiclass and binary classification, thus making the results still poor. The results for classification were promising, this can help further improve research on TabNet as an architecture for IDS.