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Updated: Oct 16, 2025

Design and Analysis for Fall Detection System Simplification

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Published on: April 6, 2020

An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning

Taher Al-Shehari¹, Rakan A Alsowail¹

¹Computer Skills, Self-Development Department, Deanship of Common First Year, King Saud University, Riyadh 11362, Saudi Arabia.

Entropy (Basel, Switzerland)

|October 23, 2021

Summary

This study introduces a machine learning model to detect insider data leakage, a critical cybersecurity threat. The model effectively identifies malicious employee data exfiltration before departure, achieving high accuracy.

Keywords:

data leakage detection insider threat detection machine learning model one-hot encoding oversampling technique

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Cybersecurity
Machine Learning
Data Science

Background:

Insider threats pose significant cybersecurity risks, often exceeding external attack damage.
Existing insider threat detection methods often overlook specific attack vectors like data leakage.
Malicious insiders can inflict severe damage, particularly when exfiltrating data before leaving an organization.

Purpose of the Study:

To propose a machine learning model for detecting insider data leakage incidents.
To address detection biases caused by encoding and dataset imbalance.
To identify the most accurate classifier for detecting data leakage by departing insiders.

Main Methods:

Utilized feature scaling and one-hot encoding to mitigate detection bias.
Employed Synthetic Minority Oversampling Technique (SMOTE) to address dataset imbalance.
Applied and compared well-known machine learning algorithms on the CMU-CERT Insider Threat Dataset.

Main Results:

The proposed model achieved an AUC-ROC value of 0.99 in detecting insider data leakage.
Demonstrated superior performance compared to existing approaches on the same dataset.
Successfully addressed bias and class imbalance issues in insider threat detection.

Conclusions:

The developed machine learning model offers an effective solution for detecting insider data leakage.
The methods employed enhance the reliability and accuracy of insider threat detection systems.
This research contributes to building more robust defenses against sophisticated insider attacks.