Advanced Anomaly Detection Framework Using CNN-Grid Autoencoder Integration and Recursive Fuzzy Feature Selection Approach

Basit Öğe Kaydı
dc.contributor.authorAl-Dulaimi, Reem Talal
dc.contributor.authorKurnaz Türkben, Ayça
dc.contributor.authorHussein, Mohammed Kareem
dc.contributor.authorTariq Kalil Al-Khayyat, Ali
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2025-08-14T17:32:50Z
dc.date.available2025-08-14T17:32:50Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.descriptionConference name : 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2025 Conference city : Ankara Conference date : 23 May 2025 - 24 May 2025 Conference code : 209351
dc.description.abstractNetwork intrusion poses a significant threat to data privacy and security in shared networks, often leading to cyber-attacks that compromise both system integrity and user data. Today's digital environment is seeing an increase in unauthorized actions, such as credential theft, illegal access, and data tampering. Despite the availability of various intrusion detection methodologies, accurately identifying and mitigating such threats remains a critical challenge. To address this issue, this paper proposes an automated attack classification model designed to enhance classification accuracy while minimizing errors based on input parameters. The proposed approach presents an innovative system for detecting network intrusions based on deep learning that integrates a fuzzy optimization method. The methodology begins with data pre-processing, including data cleansing and temporal smoothing, followed by feature extraction using Grid Convolutional Neural Networks (Grid-CNN). Optimal features are selected through a recursive multi-level fuzzy optimization algorithm. Finally, attack classification is performed using the SoftMax layer of the Grid-CNN architecture. The model is evaluated on the UNSW-NB15 dataset, with performance metrics including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed model achieves an accuracy of 96.02%, outperforming existing models in terms of both accuracy and robustness. This study highlights the potential of deep learning and fuzzy optimization in enhancing network intrusion.
dc.identifier.citationAl-Dulaimi, R. T., Türkben, A. K., Hussein, M. K., Al-Khayyat, A. T. K., & Ucan, O. N. (2025, May). Advanced Anomaly Detection Framework Using CNN-Grid Autoencoder Integration and Recursive Fuzzy Feature Selection Approach. In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) (pp. 1-9). IEEE. 10.1109/ICHORA65333.2025.11017049
dc.identifier.doi10.1109/ICHORA65333.2025.11017049
dc.identifier.isbn9798331510886
dc.identifier.scopus2-s2.0-105008420356
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5880
dc.indekslendigikaynakScopus
dc.institutionauthorAl-Dulaimi, Reem Talal
dc.institutionauthorKurnaz Türkben, Ayça
dc.institutionauthorHussein, Mohammed Kareem
dc.institutionauthorTariq Kalil Al-Khayyat, Ali
dc.institutionauthorUçan, Osman Nuri
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofICHORA 2025 - 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGrid-CNN
dc.subjectNetwork intrusion
dc.subjectRecursive Multi-Level Fuzzy
dc.subjectUNSW_NB15 dataset
dc.titleAdvanced Anomaly Detection Framework Using CNN-Grid Autoencoder Integration and Recursive Fuzzy Feature Selection Approach
dc.typeConference Object

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu