Towards Future: What is an ML Powered NGFW?

Towards Future: What is an ML Powered NGFW? 

An ML-Powered NGFW is an improved version of the standard firewall that uses machine learning (ML) techniques to improve security. Unlike traditional firewalls, which respond to threats after they occur, an ML-Powered NGFW is proactive. It constantly learns from tremendous amounts of data to detect threats on various fronts.

Towards Future: What is an ML Powered NGFW?
Fig 1.1- ML-Powered NGFW

PaloAlto networks is the first vendor who incorporating machine learning techniques directly into the firewall code, it can inspect files as they are downloaded and block them immediately if they are harmful. This near-zero delay between visibility and prevention greatly enhances security. 

Malware frequently targets a single victim and then spreads. In an ML-Powered NGFW, ML algorithms are included in the firewall code. This enables the firewall to check files during the download process and immediately reject any harmful information, eliminating the need for offline tools.

Skilled cybercriminals develop new malware varieties. Traditional signature-based systems require time to update and deploy new signatures. In comparison, an ML-Powered NGFW provides signature changes within seconds after ML analysis. 

This ensures that new risks are halted at the first user, and that future mutations are automatically prevented.

PaloAlto Networks ML-powered NGFWs Benefits

  • Blocks up to 95% of advanced threats by implementing inline machine learning (ML) in the firewall's core to deliver real-time threat protection against unknown attacks.
  • It analyzes traffic patterns using network telemetry data and integrates it with existing Palo Alto statistics.
  • Behavioral analysis assists in the detection of threats to IoT devices without the need for additional sensors. Palo Alto Networks IoT security uses ML to provide total device visibility, indicate abnormalities and vulnerabilities, and recommend security policies.
  • A mix of cloud-based CPU and ML aids in the detection of new assaults while also recommending policy adjustments. It saves time and minimizes human mistake, which is a major cause of breaches.
  • Zero-delay signatures and action to prevent cyber-attacks in real time resulted in a 99.5% reduction in affected systems.

ML-powered NGFW

PaloAlto Networks ML-powered NGFWs Disadvantages

There night be some of the disadvantages of these NGFWs as they are no so much matured to understand the threats fully

ML-powered NGFWs Disadvantages

PaloAlto Networks ML-powered NGFWs 

PaloAlto ML-powered NGFWs use Nova, the 11.0 release PAN-OS software, to help stop the most evasive phishing, ransomware and DNS-based threats more effectively and efficiently. In a threat environment where evasive attacks make detection much more difficult, ML-Powered NGFWs use inline deep learning to stop zero day threats in zero time.
  • PA-1400 Series (PA-1420 and PA–1410)
  • PA-5400 (PA-5440)
  • PA-400 Series (the PA-445 and PA-415)

PaloAlto' s unique Single Pass Parallel Processing (SP3) Architecture running on all of our fourth-generation ML-Powered Next-Generation Firewalls, organizations get consistent performance regardless of the number of security services enabled. This allows consolidation of multiple products over time without having to resize the firewalls or choose between performance or security.