Why Does Machine Learning Matter in Cybersecurity?

Over the past two decades, network security specialists have worked to combat cyberattacks by reducing the time required to detect and neutralise threats. Although response times have decreased from days to hours or minutes, cyberattackers have not given up. Cyberattacks have grown more common and sophisticated, with the capacity to devastate corporations, government organisations, and utilities in seconds.

Most security experts agree that the sector has been playing defence against cyberattacks for quite some time. Machine learning (ML) techniques, on the other hand, are being used to identify network breaches, malware, and phishing attempts, giving security experts a powerful new weapon at their disposal.

Intelligent Network Security

ML provides security specialists and their businesses with more control over network security. Because machine learning can predict and combat attacks in near-real time, network security becomes intelligent, shifting network protection from a reactive to a proactive state. Here's how it's done:

• Keep an eye out for new dangers: IT teams get an edge when they can transition from defence to offensive in the face of cyber threats. When employed at the network's core, ML algorithms can detect both known and undiscovered threats. This is true for ML-Powered Next-Generation Firewalls, which employ machine learning inline to fight against 95% of unknown attacks. Threats that are not detected by inline machine learning may be eliminated in near-real time by using zero-delay signature updates.
• Gain enterprise-wide visibility and security: Without network visibility, security teams cannot be effective. As more Internet of Things (IoT) devices, such as cameras and tablets, are added to the network, the ability to view all apps, users and devices has become more vital. ML-based security may provide end-to-end device visibility and aid in the detection of network abnormalities.
• Enhance security policies: ML modelling can convert telemetry data into suggested security policy modifications. This feature is especially significant for IoT security since it enables security experts to analyse and implement IoT security policy recommendations for all devices in a network. As a consequence, the enterprise's security is enhanced, and security personnel save time.
• Reduce the number of breaches caused by human mistakes: Professionals in network security recognise how difficult it may be to keep up with the pace of change in applications and devices. Furthermore, manually changing security rules is typically time-consuming and error-prone. When employed at the heart of network security, ML can suggest and disseminate solid security rules, saving security teams hours of manual updates and lowering the possibility of human mistakes.