Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm

Enhancing Insider Threat Detection in Imbalanced Cybersecurity Settings Using the Density-Based Local Outlier Factor Algorithm

In today’s interconnected world, cybersecurity has emerged as a critical domain for ensuring the integrity, confidentiality, and availability of digital assets. Within this sphere, insider threats represent a unique and particularly insidious class of security risks, originating not from...

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Bibliographic Details
Main Authors: Taher Ali Al-Shehari, Domenico Rosaci, Muna Al-Razgan, Taha Alfakih, Mohammed Kadrie, Hammad Afzal, Raheel Nawaz
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Machine learning
insider threat detection
local outlier factor algorithm
data imbalance addressing
Online Access:https://ieeexplore.ieee.org/document/10459083/

Internet

https://ieeexplore.ieee.org/document/10459083/

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