Security News > 2023 > February > The Future of Network Security: Predictive Analytics and ML-Driven Solutions

To address these challenges, organizations are turning to predictive analytics and Machine Learning driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad. ML-driven network security solutions in cybersecurity refer to the use of self-learning algorithms and other predictive technologies to automate various aspects of threat detection.

In summary, the mentioned drawbacks of rule-based security solutions highlight the significance of taking a more holistic approach to network security, which should nowadays include ML-powered Network Detection and Response solutions to complement traditional detection capabilities and preventive security measures.

Automated analysis of anomalous behavior: AI enables a much-required health monitoring of network activity by utilising the analysis of normal network traffic as a baseline.

Key questions to be answered include "What is the activity of other clients in the network?" and "Is a client's behavior in line with its own previous activities?" These approaches allow for the detection of unusual behaviors like domain-generated algorithms domains, volume-based irregularities in network connections, and unusual communication patterns in the network.

When it comes to ML-driven Network Detection & Response solutions that incorporate the outlined benefits, ExeonTrace stands out as a leading network security solution in Europe.

Based on award-winning ML algorithms, which incorporate a decade of academic research, ExeonTrace provides organizations with advanced ML threat detection capabilities, complete network visibility, flexible log source integration and big data analytics.

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