Insider threats pose a significant challenge to organizational cybersecurity, often leading to catastrophic financial and reputational damages. Traditional tools such as firewalls and antivirus systems lack the sophistication needed to detect and mitigate these threats in real time. This paper introduces a machine learning-based system that integrates real-time anomaly detection with dynamic user profiling, enabling the classification of employees into categories of low, medium, and high risk. The system was validated using a synthetic dataset, achieving exceptional accuracy across machine learning models, with XGBoost emerging as the most effective.

In the dynamic realm of cybersecurity, awareness training is crucial for strengthening defenses against cyber threats. This survey examines a spectrum of cybersecurity awareness training methods, analyzing traditional, technology-based, and innovative strategies. It evaluates the principles, efficacy, and constraints of each method, presenting a comparative analysis that highlights their pros and cons. The study also investigates emerging trends like artificial intelligence and extended reality, discussing their prospective influence on the future of cybersecurity training. Additionally, it addresses implementation challenges and proposes solutions, drawing on insights from real-world case studies. The goal is to bolster the understanding of cybersecurity awareness training's current landscape, offering valuable perspectives for both practitioners and scholars.

Insider threats pose a significant challenge to organizational cybersecurity, often leading to catastrophic financial and reputational damages. Traditional tools like firewalls and antivirus systems lack the sophistication needed to detect and mitigate these threats in real time, particularly when faced with subtle and evolving malicious behaviors. This paper introduces a machine learning-based system that integrates real-time anomaly detection with dynamic user profiling, enabling the classification of employees into risk categories—low, medium, and high. By leveraging continuous monitoring and adaptive algorithms, the proposed tool provides immediate alerts and actionable insights, significantly enhancing organizational responsiveness to insider threats. The system’s efficacy was validated using a synthetic dataset, achieving exceptional accuracy across machine learning models, with XGBoost emerging as the most effective for detection and classification. This work addresses critical gaps in traditional and existing machine learning methods, offering a proactive, scalable, and fully automated solution. Future research will explore real-world data validation and incorporate psychological profiling to further augment detection capabilities, setting a foundation for the next generation of insider threat management systems.

Biometric authentication is widely regarded as more secure and reliable than conventional approaches like passwords and PINs. Nonetheless, many current systems rely on active user participation, such as fingerprint scanning or facial recognition, which can disrupt tasks, increase the likelihood of errors, and raise privacy concerns. To address these challenges, this study introduces a continuous, seamless authentication framework that utilizes vital signs for passive identity verification across various activities, including resting, walking, and running. The framework analyzes physiological indicators such as Heart Rate (HR), Heart Rate Variability (HRV), Skin Temperature, Peripheral Oxygen Saturation (${\mathrm{SpO}}_2$), and Breathing Rate to provide zero-effort authentication without requiring user intervention. Multiple machine learning algorithms, including Decision Tree, Random Forest, XGBoost, Gradient Boosting, and K-Nearest Neighbors, were implemented and compared to identify the most effective predictive model. The methodology involved data collection, preprocessing, model construction, evaluation, and comparison. Experimental results revealed that the XGBoost Classifier achieved the highest accuracy at 96%. Overall, the proposed framework demonstrates strong reliability, scalability, adaptability, and flexibility, making it suitable for practical deployment. By continuously verifying identity without interrupting user activity, it improves both security and usability, offering a modern and convenient alternative to traditional authentication methods.