A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR CYBERSECURITY ANALYST OPERATIONS IN THREAT DETECTION

Abstract : The growing complexity and volume of cyber threats have significantly increased the demands on cybersecurity analysts, necessitating the adoption of automated, intelligent systems to aid in real-time threat detection and response. Machine learning (ML) algorithms have emerged as powerful tools for identifying anomalies, detecting intrusions, and classifying threats across diverse network environments. This paper presents a comprehensive comparative study of several widely used machine learning algorithms—namely Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNN)—evaluating their performance in typical cybersecurity analyst workflows, particularly in intrusion detection systems (IDS) and threat intelligence platforms. Using benchmark datasets such as NSL-KDD and CICIDS2017, we examine the accuracy, precision, recall, F1-score, and computational efficiency of each algorithm in detecting known and unknown attack vectors. The study also analyzes the interpretability and practical deployment challenges of each model within Security Operations Centers (SOCs), considering the need for real-time analysis and analyst-friendly interfaces. Results show that while deep learning models like DNN outperform others in terms of detection accuracy, traditional algorithms like Random Forests and Decision Trees offer greater transparency and lower computational costs, making them more suitable for real-time applications in resource-constrained environments. Furthermore, hybrid approaches and ensemble techniques demonstrate improved robustness in detecting sophisticated multi-vector attacks. The paper discusses trade-offs between detection performance and operational feasibility, providing insights into how cybersecurity analysts can integrate specific ML models based on threat profiles, organizational priorities, and infrastructure capabilities. Finally, recommendations are made for the development of adaptive, analyst-centric ML frameworks that support continuous learning and automated threat prioritization. This study aims to bridge the gap between academic advancements in machine learning and their practical application in day-to-day cybersecurity operations, contributing to the development of more intelligent, scalable, and resilient defense mechanisms against emerging cyber threats.

Keyword Cybersecurity, Machine Learning, Threat Detection, Intrusion Detection Systems (IDS), Anomaly Detection, Cybersecurity Analyst