3 contributions of Artificial Intelligence to the telecommunications sector

Artificial intelligence is increasingly used and applied in many sectors, and as it could not be less, it has entered with force in the sector of the telecommunications. In fact, the application of AI in this field is becoming increasingly popular.

Communications service providers have turned to companies offering highly personalized services to participate in the digital transformation process. AI is used for a variety of purposes as we have seen on other occasions that can range from improving customer experience to predictive maintenance to improve network reliability.

In this article we will show you through some examples how artificial intelligence can influence telecommunications companies.

THE CONTRIBUTION OF ARTIFICIAL INTELLIGENCE TO TELECOMMUNICATIONS SECURITY

As we know, artificial intelligence is based on a large amount of data and telecommunication networks are full of it thanks in particular to telemetry or performance indicators. This information makes it possible to model learning techniques taking into account a large number of variables. These models have the ability to learn from past experience so that operational errors can be detected and corrected to avoid loss of productivity.

Although the world of telecommunications is becoming increasingly complex, it is not free from attacks. Here we talk about some of them and how artificial intelligence manages to prevent them:

• Recognition of characteristics. Thanks to this great advantage that AI presents in its data analysis, it is able to detect anomalies and put in advance to be able to solve them.
• Incident response. Although it is still left under human supervision, the system could solve problems by itself, thus allowing to anticipate future attacks. The function of the Artificial Intelligence in this case is that of an assistant: to look for solutions and to control the evolution of the situation (thanks to its knowledge of statistics).
• Connectionism. Starting from a particular case, a rule is found. The AI intervenes on this occasion by identifying rules between data through the use of the Deep Learning.
• Symbolism. It is in charge of deduction. Contrary to what was explained in the previous point, it starts from a rule and from this, it finds the particular case. Thanks to what the connectionist model has found, we know a series of incidents and their process. When faced with an attack, we compare it with others so that it can be identified and solved.

One of the next challenges of the telecommunications security will be to be able to detect behavioral variations, detecting possible attacks thanks to artificial intelligence.

IA FOR NETWORK OPTIMIZATION

In addition to serving to detect attacks, telecommunications companies can use advanced AI algorithms to detect and predict network failures and resolve problems before they negatively impact consumers.

Depending on the results to be achieved, there are different models such as:

• Routing algorithmsThe network routing system: used to dynamically program the optimal route in the network. It is based on advanced analytics that try to make predictions and look for patterns in the data.
• Reinforcement learning. In this case, artificial intelligence reproduces a simulation of the user's behavior and optimizes the operations to be performed in order to achieve greater satisfaction with the user experience.

Prediction models: has improved on traditional techniques for predicting future traffic, facilitating planning and anticipating consumer behavior.

Network operators can leverage information from data collected by artificial intelligence to reduce waiting times, optimize load balancing and improve traffic demand forecasting, thereby improving customer satisfaction.

ARTIFICIAL INTELLIGENCE FOR TRAFFIC CLASSIFICATION AND FLOW GROUPING.

Continuing with the pattern detection performed by Artificial Intelligence, this can be used -among other things- for traffic profiling purposes and this allows:

1. Identification of session clustersThe strategic management of the sessions according to the characteristics of the cluster.
2. Identification of clusters based on traffic flowNetwork conditions: identify and classify network conditions according to traffic flow.
3. Malicious session detection.

In this way an algorithm can be created and prepared that identifies groups of observations that share common characteristics to classify the data. Today there are some companies that are starting to implement this software.

It is undeniable that the telecommunications sector and Artificial Intelligence must go hand in hand to continue to progress. We will follow this relationship very closely to see what advances are made thanks to this union.

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