Application of Machine Learning Technologies for Managing Multifactor Threats in an Integrated Model of Cognitive Security Center at Defense Industry Enterprise
Abstract
The presented innovative model of the cognitive security center, based on machine learning technologies, represents a significant advancement in effectively managing multifactor threats in defense-industrial complex enterprises. This article provides a detailed examination of key elements of this model, including data analysis, anomaly detection, threat response, classification and optimization, as well as the notification system.
Particular attention is given to the model's ability to integrate data from various sources in real-time, enabling swift responses to diverse threats and providing a comprehensive overview of the enterprise's security. The model effectively demonstrates the application of machine learning algorithms, efficiently processing anomalies and responding to threats, offering real-time operational security management solutions.
Additionally, the article underscores the importance of the dynamic adaptation of machine learning algorithms to new threats, imparting resilience to the system in a constantly changing security environment. Efficient threat response management is ensured through automated security protocols, expediting decision-making processes and significantly reducing potential risks for the enterprise.
A crucial component of the model is the role of the notification system, playing a key role in operational communication with security personnel and responsible structures upon threat detection. This facilitates swift and targeted actions, directed towards neutralizing the threat or minimizing its potential consequences. Such a modern and effective approach to security management provides a comprehensive and integrated strategy for ensuring security in defense-industrial complex enterprises, offering real-time protection.
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