APPLICATION OF MACHINE LEARNING ALGORITHMS FOR REALTIME CYBERSECURITY MONITORING

Abstract

The rapid growth of digital technologies, cloud computing, Internet of Things (IoT) devices, and interconnected networks has significantly increased the complexity and frequency of cybersecurity threats. Traditional security mechanisms often struggle to detect sophisticated attacks in real time due to the massive volume of network traffic and the constantly evolving nature of cyber threats. Consequently, organizations are increasingly adopting Machine Learning (ML) techniques to strengthen cybersecurity monitoring systems and improve threat detection capabilities. Machine learning algorithms enable automated analysis of large datasets, identification of hidden patterns, detection of anomalies, and prediction of potential security breaches with minimal human intervention. These capabilities make ML an essential component of modern cybersecurity infrastructures. Recent studies indicate that ML-based intrusion detection systems (IDS) and anomaly detection models significantly enhance the ability to identify malicious activities and unknown attack patterns in real-time environments. This study examines the application of machine learning algorithms for real-time cybersecurity monitoring. The research focuses on widely used ML techniques such as Decision Trees, Random Forests, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and ensemble learning methods for intrusion detection and cyber threat classification. The study investigates the design of intelligent cybersecurity monitoring systems capable of collecting network traffic data, extracting relevant features, classifying threats, and generating automated alerts. Publicly available cybersecurity datasets such as NSL-KDD, CICIDS2017, and UNSW-NB15 are considered for model development and evaluation. The research also explores critical components of machine learning-based cybersecurity systems, including data preprocessing, feature selection, model training, performance evaluation, and real-time deployment. Metrics such as accuracy, precision, recall, F1-score, and detection rate are used to assess algorithm performance. Existing research demonstrates that machine learning approaches provide higher detection accuracy and better adaptability to emerging threats compared to conventional signature-based security systems. Despite their advantages, ML-based cybersecurity systems face challenges related to adversarial attacks, false positives, data privacy concerns, and computational complexity. Future developments involving deep learning, federated learning, explainable AI, and adaptive threat intelligence systems are expected to further improve cybersecurity monitoring effectiveness. The study concludes that machine learning algorithms represent a powerful solution for enhancing real-time cybersecurity monitoring, enabling organizations to detect, prevent, and respond to cyber threats more efficiently in increasingly complex digital environments.