Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories
Abstract
:1. Introduction
1.1. Research Rationale Driving the Current Study
1.2. Literature Review of Empirical Studies Focused on Cybercrime Risk
1.3. Literature Review of Non-Empirical Studies Focused on Cybercrime Risk
1.4. Personality Theory Overview from the Literature
1.5. Literature Summary and Research Question in the Current Study
2. Materials and Methods
2.1. Research Design Summary
- (1)
- Design study (ideology: pragmatic, strategy: abductive, nature: exploratory);
- (2)
- Review RQ-related literature (abductive strategy focused on relevant topics);
- (3)
- Identify the population (sample technique, unit of analysis, analysis level, data);
- (4)
- Methods and materials (data types, relevant method ML, ethics, permission);
- (5)
- ML: methods plan phase (semi-supervised ML, some labels, classification);
- (6)
- ML: explore data phase (data collection/cleaning, CHAID decision tree);
- (7)
- ML: develop models phase (credibility, validity: 20-folds, reliability checks);
- (8)
- Document research study (quality assurance, dissemination to journal).
2.2. Research Ideology, Strategy, and Overall Methodology
2.3. Literature Review Approach
2.4. Population and Sampling
2.5. Ethics and Big Data Access Permission
2.6. ML Technique Planning
2.7. Big Data Exploration with Selected ML Techniques
2.8. Big Data Analysis, Training Model Development, and Evaluation with ML
- 0.9–1.0 = outstanding quality;
- 0.8–0.9 = excellent/superior quality;
- 0.7–0.8 = acceptable/fair quality;
- 0.6–0.7 = poor quality;
- 0.5–0.6 = zero quality.
- 0.00–0.20 = low agreement with the null model, benchmark acceptable score;
- 0.20–0.40 = weak agreement with the null model, baseline acceptance score;
- 0.40–0.60 = moderate agreement with the null model, borderline to poor score;
- 0.60–0.80 = good agreement with the null model, poor score;
- 0.80–1.00 = high agreement with the null model, unacceptable score.
2.9. Plan to Finalize and Disseminate Research Study Findings
3. Results and Discussion
3.1. Quality Scores from ML Training Model Development and Testing
3.2. Cybercrime Risk Keyword Association Analysis
3.3. Cybercrime Risk Learning Tree Analysis
3.4. Cybercrime Risk Feature Scatter Plot Analysis
3.5. Cybercrime Risk Feature Heat Map Analysis
4. Conclusions
4.1. Literature Contrasts, Detailed Implications, and Generalizations
4.2. Limitations and Caveats
4.3. Future Study Recommendations
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Neuroticism | Openness | Agreeableness | Conscientiousness | Extroversion |
---|---|---|---|---|
stressed | create creative | interested | prepared | party parties |
worried | imagination | sympathize | detail detailed | conversation |
upset | ideas | comfort | exact | attention |
pissed | complexity | gratitude | accurate | talk discuss |
moody | new newness | follow | plan | friends |
angry | try | accept | attention | social |
revenge | experiment | listen | quality | fun |
Predicted | |||
---|---|---|---|
Actual | 0 (Risk = No) | 1 (Risk = Yes) | Total |
0 (risk = no) | 63.6% | 6.1% | 69.7% |
1 (risk = yes) | 6.1% | 24.2% | 30.3% |
Total | 69.7% | 30.3% | 100% |
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Strang, K.D. Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories. Big Data Cogn. Comput. 2024, 8, 37. https://doi.org/10.3390/bdcc8040037
Strang KD. Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories. Big Data and Cognitive Computing. 2024; 8(4):37. https://doi.org/10.3390/bdcc8040037
Chicago/Turabian StyleStrang, Kenneth David. 2024. "Cybercrime Risk Found in Employee Behavior Big Data Using Semi-Supervised Machine Learning with Personality Theories" Big Data and Cognitive Computing 8, no. 4: 37. https://doi.org/10.3390/bdcc8040037