ALGORITHMICAL APPROACHES TO ANOMALIES DETECTION BASED ON MACHINE LEARNING
DOI 10.31673/2412-4338.2025.026117
Abstract
Abstract. Anomaly detection in cybersecurity is a critically important process aimed at identifying atypical patterns of behavior or activity that significantly differ from established, normal operating procedures within an information system or computer network. These deviations from the norm can serve as early indicators of potential security threats, ranging from unauthorized intrusion attempts and malware distribution to exploitation of existing vulnerabilities in software or system configuration. Timely and effective detection of such anomalous events provides an opportunity to respond quickly to threats, prevent their further development, and minimize potential risks to the confidentiality, integrity, and availability of critical data and the organization’s digital infrastructure.
Modern cybersecurity anomaly detection systems increasingly use sophisticated machine learning algorithms to effectively recognize complex and subtle patterns of behavior. Unlike traditional methods that rely heavily on predefined rules and static thresholds, machine learning algorithms have the ability to learn from large amounts of data, which allows them to detect new and previously unknown types of attacks that static rules may not be able to handle. In cases where cybercriminals are constantly improving their methods and tools, the ability of machine learning algorithms to adapt to new threats by analyzing data on previous attacks and normal behavior becomes extremely valuable. These algorithms can detect subtle deviations that may be missed by systems based on strict rules, thereby increasing the overall effectiveness of intrusion detection and data leakage prevention systems. The use of machine learning allows you to build more intelligent and proactive security systems that can effectively counter modern cyber threats.
Keywords: anomaly detection, machine learning, information security, Z-score, statistical methods, neural networks, cyberattacks.
