PeerJ Computer Science, Год журнала: 2024, Номер 10, С. e2290 - e2290

Опубликована: Сен. 9, 2024

The adoption and integration of the Internet Things (IoT) have become essential for advancement many industries, unlocking purposeful connections between objects. However, surge in IoT has also made it a prime target malicious attacks. Consequently, ensuring security systems ecosystems emerged as crucial research area. Notably, advancements addressing these threats include implementation intrusion detection (IDS), garnering considerable attention within community. In this study, aim to enhance network anomaly detection, we present novel approach: Deep Neural Decision Forest-based IDS (DNDF-IDS). DNDF-IDS incorporates an improved decision forest model coupled with neural networks achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, Random Forest Feature Importance (RFFI), our objective is streamline training prediction processes, overall performance, identify most correlated features. Evaluation on three diverse datasets (NSL-KDD, CICIDS2017, UNSW-NB15) reveals impressive ACC values ranging from 94.09% 98.84%, depending dataset method. achieves remarkable time 0.1 ms per record. Comparative analyses other recent random Convolutional Networks (CNN) based models indicate that performs similarly or even outperforms them certain instances, particularly when utilizing top 10 One key advantage lies its ability make accurate predictions only few features, showcasing efficient utilization computational resources.

Язык: Английский