DEVELOPMENT OF SOFTWARE SYSTEM FOR SECURITY ASSURANCE BASED ON AUTOMATIC DETECTION OF ANOMALIES AND FAKE TEXTS USING MACHINE LEARNING ALGORITHMS

Zolotukhina Maria Alexandrovna

The anomaly detection system in text data, developed using modern technologies and algorithms, is an innovative approach to identifying hidden threats, including fake text data in various information sources. The development plays a key role in ensuring the national security of the state, offering innovative methods for detecting and preventing fake data in the digital sphere. The project covers a wide range of threats, including phishing attacks, disinformation, and data leaks. The unique method of this system includes an in-depth analysis of textual features in order to identify the characteristic features and patterns inherent in fake or manipulated texts. The effectiveness of the system is ensured by the use of an ML model learning algorithm, which ensures the accurate identification of entities indicating the fake nature of the text. The integration of the system with corporate information systems allows the analysis of data from various sources, including websites, social media, and many others. This approach is an integral component for ensuring a high level of protection of companies (organizations) from fraud and maintaining trust, which is of critical importance in the modern information environment. The development provides high adaptability to new threats and changes in text strategies, which makes it a powerful tool in an ever-changing environment to combat anomalies.

Keywords:ML algorithms, nlp tasks, anomalies, software, security, fake, autocorrelation, static analysis, EIS.