Jan 1, 2023 · Robust machine learning and deep learning models for identifying network intrusion and attack types are proposed in this paper. Proposed models have experimented with the …

In response, this study investigates the effectiveness of machine learning algorithms for enhancing intrusion detection systems in network security. Our findings reveal that Random …

Dec 23, 2024 · We trained classification models and optimised their parameters using cross-validation and grid search techniques. The models exhibited robustness in identifying unseen …

We trained classification models and optimised their parameters using cross-validation and grid search techniques. The models exhibited robustness in identifying unseen attacks, enabling...

3 days ago · Numerous machine learning models exhibit binary classification performance in terms of precision, recall, and F1-Score, as illustrated in Fig. 2. MLP had the best accuracy of …

1 day ago · The anomaly-and signature-based IDS for network security using hybrid inference systems,” Math. Probl. Eng. ... Supervised machine learning models for prediction of COVID …

Network security is a top priority in today’s digital economy, where intrusions grow in sophistication and size. To safeguard network infrastructure and data, i.

In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the …

Mar 4, 2025 · Abstract: This study uses various models to address network traffic classification, categorizing traffic into web, browsing, IPSec, backup, and email. We collected a …

One such promising method is ensemble learning, a powerful machine learning approach that combines multiple models to improve predictive performance. This article seeks to shed light …