Machine learning in networking

Usage of Machine Learning in Networking

Machine learning in networking is an application of artificial intelligence (AI) that offers systems the ability to learn automatically and improve from experience without explicit instructions. Machine learning focuses on the innovations of computer programs that can get data and use it to learn for future tasks.

Machine learning driven-data tools are great at learning fundamental network actions and highlighting fluctuations relative to it. This consideration drives the service of machine learning in networking for these areas:

  • Health management
  • Performance management
  • Security

Machine Earning Solving Problems in Networking:

  • Network Security
  • Malicious traffic detection
  • Malware identification
  • Data loss prevention
  • Traffic Classification
  • Application identification
  • QoS policies
  • Traffic engineering
  • Optimization/ predictive maintenance
  • Log analysis

Applications of Machine Learning:

  • Virtual personal assistants. Siri, Alexa, Google
  • Predictions
  • Videos surveillance
  • Social media services
  • Email spam and malware filtering
  • Online customer support
  • Search engine result refining
  • Product recommendations
  • Online fraud detection

Machine Learning in Networking Security:

Network prevention is not a single object but a set of various solutions that focus on protocols such as wireless, Ethernet, even virtual networks like SDNs, or SCADA.

Network prevention refers to well-known Intrusion Detection System (IDS) processes. Some of them have been using machine learning for years and solve issues with signature-based techniques.

Machine learning in network security signifies new processes called Network Traffic Analytics (NTA) directed to a detailed analysis of all the traffic at each layer and identify threats and deviations.

Machine Learning Helping in Network Security:

  • Regression to predict the network packet parameters and compare them with the normal ones
  • Classification to identify different classes of network attacks such as scanning and spoofing
  • Clustering for forensic analysis.

Benefits of Machine Learning in Networking:

Machine learning uses algorithms to receive data, learn from it, and predict without explicit instructions. With artificial intelligence, we can leverage tasks through machine learning in the networking area:

  • Cybersecurity practices

Machine learning is fundamental in building a secure network architecture, and most organizations consider it a top priority. It enables security automation and serves in different techniques such as data classification, filtering, processing, and minimizing the workload of IT teams.

  • Predict User Experience to Adjust Bandwidth Demand

When traffic comes to the network, it is difficult to analyze DDoS attacks through widespread downloading and real traffic. The self-driving networks will predict performance problems before user affection.

  • Self-Correct for Maximum Uptime

Through intelligent AI software with machine learning techniques, it enables networks to have a program for self-correction to maximize uptime. 

  • Instantly Find Root Causes

Artificial intelligence can leverage multiple data-mining techniques to analyze the terabytes of data in a matter of minutes. It enables IT, experts, to instantly analyze network objects such as OS, device type, access point, or switch. It is most related to a network problem. It assures IT, groups, to encourage resolution of this problem.