What does Hillstone Networks do regarding IoT Firewalls or OT Firewalls?
Comprehensive IoT Security
IoT Security
• Identify IoT devices such as IP Cameras and Network Video Recorders. You get support on monitoring results based on filtering conditions, including device type, IP-address, status, etc.
• Support customised whitelists
Note, First contact your reseller or Hillstone support team or download the IoT identify image for docker.
High-Performance Hardware Architecture
The future-ready A-Series features compact form factor anda powerful computing foundation that ensures high performance with uncompromising security. A-Series NGFWs offer robust performance for firewall throughput, concurrent and new sessions, and blazing fast performance for application layer, which is critical in meeting the needs of current security environments. Powered with Hillstone proprietary hardware acceleration engine, A-Series NGFW high-end models enable fast packet forwarding with high throughput and ultra-low latency by traffic offloading. It also offers a friendly software ecology for third-party integration to support additional security features if desired. All rackmount models feature front and rear ventilation to assist in heat dissipation, which is a concern in networks of almost any size.
Rich Logs for Intelligent Analysis and Reporting
Hillstone Next Generation Firewalls provides administrators and operators high visibility and comprehensive reports with threat analysis, traffic analysis, attack breakdown and threat control. Its log aggregation capability allows logs to be aggregated from multiple dimensions, which helps operators easily identify suspicious anomalies or find false positives from logs, and then tune the policies accordingly.
Machine-Learning-Driven Security Rule Optimization and Unknown Attack Defense
In addition to general protection based on rules and scripts for known attacks, Hillstone next generation firewalls auto-learning capability helps mitigate unknown threats to protect specific applications from zero-day attacks. Its ML-based model learns from the data of normal traffic such as parameter length, HTTP methods, etc., tunes itself based on the test results as well as input from administrators, and continues updating the learning models and optimising Security.
