Hybrid Intrusion detection model-based density clustering approach and deep learning for detection of malicious traffic over network

Ola Ali Obead and Hakem Beitollahi

Data and Metadata, 2025, vol. 4, 739

Abstract: Intrusion detection in modern network environments poses significant challenges due to the increasing volume and complexity of cyber-attacks. This study proposes a hybrid approach integrating density-based clustering with deep learning to identify malicious traffic over the network. The proposed framework consists of two steps: clustering and classifying data. in clustering, the proposed model uses density clustering techniques to pre-process and segment network traffic into coherent clusters, thereby reducing data noise within clusters. The deep learning model analyses these clusters, accurately distinguishing between benign and malicious activities. The proposed model was tested over the benchmark dataset CIRA-CIC-DoHBrw-2020. The performance of the proposed model compared with standard machine learning models and the number of states of the artworks. The experiment result demonstrates that our hybrid model significantly improves detection accuracy and reduces false-positive rates compared to existing methods .

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:739:id:1056294dm2025739

DOI: 10.56294/dm2025739

Access Statistics for this article

More articles in Data and Metadata from AG Editor
Bibliographic data for series maintained by Javier Gonzalez-Argote ().