Severity-Regularized Deep Support Vector Data Description with Application to Intrusion Detection in Cybersecurity

Taha J. Alhindi ()
Additional contact information
Taha J. Alhindi: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2025, vol. 13, issue 23, 1-18

Abstract: Anomalies in real systems differ widely in impact, as such, missing a high-severity event can be far costlier and consequential than flagging a benign outlier. This paper introduces Severity-Regularized Deep Support Vector Data Description, an extention of deep support vector data description that incorporates severity for various anomaly types, reflecting the application-specific importance assigned to each type. The formulation retains the well-known deep support vector data description decision geometry and scoring system while allowing for specific control over the balance between false alarm rate and the prioritization of detecting anomalies with greater impact. In the proposed loss function, we introduce regularizing parameters that control the importance assign to each anomaly type. Experiments are carried out on a demanding simulated dataset and a real-world intrusion detection case study utilizing the Australian Defence Force Academy Linux Dataset. The results demonstrate the effectiveness of the proposed approach in detecting highly severe anomalies while maintaining competitive overall performance.

Keywords: anomaly detection; cybersecurity; Deep SVDD; host-based intrusion detection (HIDS); severity-aware intrusion detection; Severity-Regularized Deep SVDD (SR-DeepSVDD); Support Vector Data Description (SVDD) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/23/3741/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/23/3741/ (text/html)

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:gam:jmathe:v:13:y:2025:i:23:p:3741-:d:1800150

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().