Corporate network anomaly detection methodology utilizing machine learning algorithms

This study addresses the critical need for securing corporate networks against anomalies, a pressing concern in ensuring the comprehensive security of these networks. It aims to develop and validate a new machine learning-based methodology for anomaly detection that is adaptable across various corporate network environments, highlighting the method's potential practical applications. Employing a systematic approach, the research integrates system analysis of anomaly detection methodologies with an analytical review of machine learning techniques tailored for high-security measures and attack prevention in corporate networks. This dual approach ensures a robust framework for identifying and addressing network anomalies efficiently. The methodology demonstrated notable efficacy, with the proposed machine learning-based anomaly detection techniques achieving an efficiency rate upwards of 90% in identifying and categorizing network traffic types. This high level of precision allows for the effective tracking of network anomalies across diverse corporate networks and their respective devices and equipment. The findings underscore the substantial practical value of the developed methodology, offering a promising avenue for enhancing corporate network security. The implementation of this machine learning-based approach not only facilitates the timely detection of anomalies but also significantly contributes to the improvement of machine learning applications within the realm of network security. Future research could further refine these techniques, exploring scalability and real-time data analysis enhancements to bolster their effectiveness across various network configurations. [GRAPHICS]