From Simple Packet Filtering to Machine Learning-Powered Next Generation Technology
Get the e-book
IN THE EARLY DAYS OF NETWORK COMPUTING
A firewall was defined as a tool for monitoring and controlling the
flow of data traffic in and
of a network based on a set of filtering rules applied to data packets.
While it might have been appropriate at the time, this primitive tool was totally reactive.
networks have evolved, so too have firewalls which have now become more proactive.
Let’s take a look at how we got here
Following the evolution of firewalls from the earliest generation of packet filtering devices to
Unified Threat Management (UTM) devices, through Next-Generation Firewalls
(NGFW), to the most revolutionary firewalls yet: ML-Powered NGFWs:
The First Generation
Focused on inspection and filtering of packets sent into a network or system
“Stateful” filters keep track of connections between computers to judge packets
PROS: Easy to manage, straightforward use
CONS: Reactive and rules-based, easily defeated
The second Generation
Unified Threat Management
In part to respond to the rising need for application awareness in the 2000s, the second
generation of firewalls added gateway antivirus, intrusion detection, and prevention
Inspected outbound traffic as well
Web proxy filtered content
Connected remote offices using Virtual Private Networks (VPNs)
Spam filters included
PROS: More robust functionality, offered better protection than early firewalls
CONS: Little to no integration between each function, security gaps, poor performance,
complex policy management
The Third Generation
Next Generation Firewalls (NGFW)
Palo Alto Networks develops the industry’s first NGFW in 2008.
Built around integrated capabilities
Uses awareness of apps, user identity, and content
Offers enhanced application visibility and control
Supports secure, encrypted traffic via SSL/TLS
Detects and prevents advanced attacks by identifying evasive techniques and automatically
Proactive NGFWs with Machine Learning
For the first time ever, machine learning now allows Palo Alto NGFWs to deliver proactive,
real-time, and inline zero-day protection.
Identifies variants of known attacks, as well as many unknown cyberthreats
Provides complete device visibility, behavioral anomaly detection, and native enforcement
secure IoT devices without the need for additional sensors or
Serves up recommendations for policy improvements
Want to learn how Palo Alto Networks
is leveraging machine learning to protect today’s enterprises from tomorrow’s threats?
Read our e-book 4 Key Elements of an ML-Powered NGFW: How Machine Learning Is
Disrupting Network Security.