As the digital age evolves and continues to shape the business landscape, corporate networks have become increasingly complex and distributed. The amount of data a company collects to detect malicious behaviour constantly increases, making it challenging to detect deceptive and unknown attack patterns and the so-called “needle in the haystack”. With a growing number of cybersecurity threats, such as data breaches, ransomware attacks, and malicious insiders, organizations are facing significant challenges in successfully monitoring and securing their networks. Furthermore, the talent shortage in the field of cybersecurity makes manual threat hunting and log correlation a cumbersome and difficult task. To address these challenges, organizations are turning to predictive analytics and Machine Learning (ML) driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad.
The Role of ML-Driven Network Security Solutions
ML-driven network security solutions in cybersecurity refer to the use of self-learning algorithms and other predictive technologies (statistics, time analysis, correlations etc.) to automate various aspects of threat detection. The use of ML algorithms is becoming increasingly popular for scalable technologies due to the limitations present in traditional rule-based security solutions. This results in the processing of data through advanced algorithms that can identify patterns, anomalies, and other subtle indicators of malicious activity, including new and evolving threats that may not have known bad indicators or existing signatures.
Detecting known threat indicators and blocking established attack patterns is still a crucial part of overall cyber hygiene. However, traditional approaches using threat feeds and static rules can become time-consuming when it comes to maintaining and covering all the different log sources. In addition, Indicators of Attack (IoA) or Indicators of Compromise (IoC) may not be available at the time of an attack or are quickly outdated. Consequently, companies require other approaches to fill this gap in their cybersecurity posture.
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 (NDR) solutions to complement traditional detection capabilities and preventive security measures.
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