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The Impact Of Machine Learning On Networking


The two terms artificial intelligence and machine learning are much popular in the networking industry these days.

Both of these technologies offer smart data analysis. You will surely be amazed by the true value of these two technologies.

There are various optimization and operational practices due to which traditional networks suffer badly. Machine learning offers excellent solutions for networking issues and helps in effective network management.

Whether you be moving from WiFi to ethernet connection or working with some complex networks, these impressive technologies will always be there to make your work more and easier and efficient.

This is a powerful ability of computers by which they can uncover impressive patterns and hidden information with the help of complex algorithms. They can help in network optimization, service management and customer experience.

Machine learning mobile apps are already making tremendous changes in the technological world.


Network Management Issues that Machine learning Can Address

Firstly, it is essential to understand what problems we can overcome in network management.

Network managers invest a significant amount of time in sifting through the data from packets, controller, and server logs.

We need to analyze this data and find if the network needs any adjustments. After that, these change needs to be applied to solve network problems.

Now how to know whether those changes that we made earlier worked or not?

This is where machine learning steps in.

What is Machine Learning?

Machine learning refers to a data analysis method that automates the process of building analytical models.

This fantastic branch of artificial intelligence focuses on the idea that machines can make decisions with very little human intervention.

To do this first, the system analyzes data and learns from it. Then it identifies the problems, and in the end, provides the solution to the issues.

Machine learning means that a machine can make decisions using proper algorithms and equations.

Artificial intelligence refers to automating various tasks. Website chatbots and Apple’s Siri are an example of AI-based systems.


Impact on Network Management

Machine learning can predict various network issues before they even happen.

Moreover, machine learning algorithms can identify user network problems and offer solutions. Also, you don’t need to use any extra software or servers to get the data as the machine learning makes use of the data that is already on the network.

  • Machine learning can automate the network management process that we perform manually. Also in term of network enterprise, it can eliminate costly manual analysis of the network data
  • Identify the network performance issues and offer predictions to fix them.
  • Predict the network problems before they occur.
  • Machine learning systems keep an eye on the network station 24×7 and allows the network manager to know how, what, when where, and why the network problem occurred. It also tells what to do to overcome the issue.

Pattern Recognition

Machine learning is a brilliant tool to modify network traffic pattern. This can be helpful for auto-baselining. 

For example, it can pick up monthly, or weekly access patterns from the network traffic and this way create automatic baseline form those.

This helps in performance analysis and monitoring security insights too.

Anomaly Detection

ML can detect the anomalies the way users access an application and alert the operator. All of this works in real time. It can also take migitation steps and send that traffic through heavy-duty scrubbing.

Network Optimization

An AI-based system can learn how different network editing, configuration, and traffic patterns impact a network service.

Using this knowledge, it can provide useful recommendations to improve the quality of various services, just like a networking expert would give.

Addressing Multidimensional Wireless Issues

The problems that arise due to multidimensional wireless networks are quite challenging to diagnose. AI and machine learning can explore various possibilities that result in bad network experience.


Health Management

Analytics by machine learning can identify when a component of a network is in its initial stage of failure. Moreover, it can also predict when other healthy nodes will get into this problem.


Hackers are becoming more and more efficient, and due to this sometimes, it is much difficult to detect the malware. Threats are around us all the time.

ML can be of great help for the cybersecurity teams as it can spot the anomalies in network behaviors. It can detect the outliners in behavior and create a model of it. Visit this site to learn about detecting outliners.

This way, the cybersecurity team can find almost everything from hacked hardware to any employee playing with the company network.

Bug Detection

The self-learning networks make use of machine learning to detect the misbehaving networking devices that the traditional tools cannot identify.


WAN Path Optimization

Machine learning network can easily predict when there will be high traffic and take protective measures for it. It can anticipate congestion by diverging that traffic on some alternate path.

Final Words

Machine learning means getting the computer to solve various problems with minimal human intervention. This is the excellent ability of powerful computer by which they can make use of complex algorithms and mathematics to uncover patterns and information.

The network managers are always in search of finding the best and rapid solution to network problems. Machine learning helps them in solving network issues before these problems become quite large.

AI and machine learning both are capable of transforming networking. They have the tendency to identify complex network issues and answer the most critical network questions.

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