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.
Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a num…
Italian Sign Language (LIS) is the primary form of communication for many members of the Italian deaf community. Despite being recognized as a fully fledged language with its own grammar and syntax, LIS still faces challenges in gaining widespread recognition and integration into…
In the broad scientific field of time series forecasting, the ARIMA models and their variants have been widely applied for half a century now due to their mathematical simplicity and flexibility in application. However, with the recent advances in the development and efficient de…
Machine learning (ML) methods have been successfully employed to support decision-making for Software as a Service (SaaS) providers. While most of the published research primarily emphasizes prediction accuracy, other important aspects, such as cloud deployment efficiency and env…
This paper presents a novel approach to robotic control by integrating nonlinear dynamics with machine learning (ML) in an Internet of Things (IoT) framework. This study addresses the increasing need for adaptable, real-time control systems capable of handling complex, nonlinear…
Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in…