Optimized AI-Driven Intrusion Detection in WSNs: A Semi-Supervised Learning Paradigm

Clicks: 1
ID: 312918
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

Ranked #390 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 this study, we have developed an advanced semi-supervised learning model specifically designed to identify four distinct types of attacks in Wireless Sensor Networks (WSNs): Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). Our model leverages the combined advantages of supervised and unsupervised learning approaches, employing a Support Vector Machine (SVM) for the supervised aspect and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for the unsupervised component. We rigorously tested and validated our model using the NSL-KDD dataset, which highlighted its strong performance metrics, including accuracy and F1-score. Additionally, our research investigated the sensitivity of DBSCAN parameters and their effects on model accuracy, underscoring the importance of precise parameter tuning to achieve optimal results. A notable advantage of our semi-supervised approach is its capacity to manage large amounts of unlabeled data effectively, a challenge that purely supervised or unsupervised methods often face independently. By efficiently utilizing labeled data and integrating clustering techniques, our model shows improved accuracy and effectiveness in detecting intrusions within WSNs. Overall, this research advances the field of intrusion detection in WSNs by introducing a practical and effective semi-supervised learning framework. This framework enhances detection performance across various attack types and provides valuable insights into optimizing model performance through parameter sensitivity analysis and strategic dataset use.
Reference Key
imported_1777057808_69ebc01041068 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Asif Nadeem
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/683
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.