Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection
In today's digital landscape, the rapid expansion of computer networks and the increasing reliance on information technology have made network security a paramount concern. With the growing sophistication of cyber threats, traditional intrusion detection systems (IDS) face significant challeng...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
College of Education for Pure Sciences
2023-12-01
|
| Series: | Wasit Journal for Pure Sciences |
| Subjects: | |
| Online Access: | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/257 |
| _version_ | 1797288416556089344 |
|---|---|
| author | Abbas Alharan |
| author_facet | Abbas Alharan |
| author_sort | Abbas Alharan |
| collection | DOAJ |
| description |
In today's digital landscape, the rapid expansion of computer networks and the increasing reliance on information technology have made network security a paramount concern. With the growing sophistication of cyber threats, traditional intrusion detection systems (IDS) face significant challenges in effectively identifying and mitigating security breaches. To address these evolving threats, novel approaches that combine cutting-edge technologies are required. This paper explores the fusion of autoencoder based classifier to training and classifying the attacks of IDS. This approach is applied on the most meaningful feature that selected based on the pearson correlation (for continues vales) and chi-square test (for binary values). The benchmark NSL-KDD database is utilized to assess the validity of the suggested IDS. The experimental outcomes demonstrate that the designed Intrusion Detection System (IDS) attains superior classification accuracy at 92.27%, outperforming both prior research efforts and alternative classifier methods.
|
| first_indexed | 2024-03-07T18:49:07Z |
| format | Article |
| id | doaj.art-25e4f7a86c974d7aaf67aa8f88b67c78 |
| institution | Directory Open Access Journal |
| issn | 2790-5233 2790-5241 |
| language | English |
| last_indexed | 2024-03-07T18:49:07Z |
| publishDate | 2023-12-01 |
| publisher | College of Education for Pure Sciences |
| record_format | Article |
| series | Wasit Journal for Pure Sciences |
| spelling | doaj.art-25e4f7a86c974d7aaf67aa8f88b67c782024-03-02T02:02:28ZengCollege of Education for Pure SciencesWasit Journal for Pure Sciences2790-52332790-52412023-12-012410.31185/wjps.257Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature SelectionAbbas Alharan0Computer science department, Faculty of education for girls, University of Kufa, Najaf, Iraq In today's digital landscape, the rapid expansion of computer networks and the increasing reliance on information technology have made network security a paramount concern. With the growing sophistication of cyber threats, traditional intrusion detection systems (IDS) face significant challenges in effectively identifying and mitigating security breaches. To address these evolving threats, novel approaches that combine cutting-edge technologies are required. This paper explores the fusion of autoencoder based classifier to training and classifying the attacks of IDS. This approach is applied on the most meaningful feature that selected based on the pearson correlation (for continues vales) and chi-square test (for binary values). The benchmark NSL-KDD database is utilized to assess the validity of the suggested IDS. The experimental outcomes demonstrate that the designed Intrusion Detection System (IDS) attains superior classification accuracy at 92.27%, outperforming both prior research efforts and alternative classifier methods. https://wjps.uowasit.edu.iq/index.php/wjps/article/view/257Intrusion detection systemAutoencoderFeature selectionClassificationChi-squarePearson correlation |
| spellingShingle | Abbas Alharan Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection Wasit Journal for Pure Sciences Intrusion detection system Autoencoder Feature selection Classification Chi-square Pearson correlation |
| title | Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection |
| title_full | Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection |
| title_fullStr | Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection |
| title_full_unstemmed | Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection |
| title_short | Enhancing Intrusion Detection with Autoencoder Based Classifier and Statistical Feature Selection |
| title_sort | enhancing intrusion detection with autoencoder based classifier and statistical feature selection |
| topic | Intrusion detection system Autoencoder Feature selection Classification Chi-square Pearson correlation |
| url | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/257 |
| work_keys_str_mv | AT abbasalharan enhancingintrusiondetectionwithautoencoderbasedclassifierandstatisticalfeatureselection |