A virtual
private network (VPN) is programming that creates a safe and encrypted
connection over a less secure network, such as the internet. VPNs were
originally developed to provide branch office employees with safe access to
corporate applications and data. Today, VPNs are often used by remote workers
and business travelers who require access to sites that are geographically
restricted. The two most common types of VPNs are remote access VPNs and
site-to-site VPNs.
Remote access VPN
Remote access VPN
clients connect to a VPN gateway on the organization's network. The gateway
requires the device to authenticate its identity before granting access to
internal network resources such as file servers, printers and intranets.
This type of VPN
usually relies on either IP Security (IP-sec) or Secure Sockets Layer (SSL) to
secure the connection, although SSL VPNs are often focused on supplying
secure access to a single application rather than to the entire internal
network. Some VPNs provide Layer 2 access to the target network; these
require a tunneling protocol like the Point-to-Point Tunneling Protocol or
the Layer 2 Tunneling Protocol running across the base IP-sec connection. In
addition to IP-sec and SSL, other protocols used to secure VPN connectivity
and encrypt data are Transport Layer Security and OpenVPN.
Site-to-site VPN
In contrast, a
site-to-site VPN uses a gateway device to connect an entire network in one
location to a network in another location. End-node devices in the remote
location do not need VPN clients because the gateway handles the connection.
Most site-to-site
VPNs connecting over the internet use IP-sec. It is also common for them to
use carrier MPLS clouds rather than the public internet as the transport for
site-to-site VPNs. Here, too, it is possible to have either Layer 3
connectivity (MPLS IP VPN) or Layer 2 (virtual private LAN service) running
across the base transport.
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In the ever-evolving landscape of artificial intelligence (AI) and machine learning (ML), one of the most intriguing advancements is the emergence of General AI (Gen AI). To grasp its significance, it's essential to first distinguish between these interconnected but distinct technologies. AI, ML, and Deep Learning: The Building Blocks Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Machine Learning, a subset of AI, empowers machines to learn from data and improve over time without explicit programming. Deep Learning, a specialized subset of ML, involves neural networks with many layers (hence "deep"), capable of learning intricate patterns from vast amounts of data. Enter General AI (Gen AI): Unraveling the Next Frontier Unlike traditional AI systems that excel in specific tasks (narrow AI), General AI aims to replicate human cognitive abilities across various domains. I...
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