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

Clicks: 4
ID: 312926
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #254 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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.
Reference Key
imported_1777057863_69ebc047073d4 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muqaddas Salahuddin
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/554
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.