Real-Time Intrusion Detection with Deep Learning: Analyzing the UNR Intrusion Detection Dataset

Abstract

In current years, the escalation of cyber threats has underscored the need for advanced intrusion detection systems (IDS). This study explores the application of deep learning (DL) strategies to enhance IDS capabilities, utilizing the university of Nevada Reno Intrusion Detection Dataset (UNR IDD) because the benchmark. The UNR IDD dataset, known for its diverse set of network traffic patterns gives a wealthy foundation for schooling and comparing deep learning (DL) models. We investigated numerous deep learning architectures, consisting of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and synthetic Neural Networks (ANNs), as well as a hybrid version combining CNNs and RNNs. Our fashions were evaluated based on detection accuracy, false positive quotes, and computational performance. results display that deep learning techniques, particularly the hybrid model, offer full-size enhancements over traditional techniques, attaining a detection accuracy of up to 96.2% and a false positive rate as little as 1.5%. This paintings contributes to the sphere by showcasing the efficacy of superior neural community techniques in actual-world intrusion detection scenarios, paving the manner for greater sturdy and adaptive safety solutions.