Use Case

Anomaly Detection on IoT LoRa Traffic

The Challenge when you have an existing IoT infrastructure with an ever growing number of devices to manage It is important for you to provide an excellent quality of service and also being able to anticipate network malfunctions is now becoming a key element. After all keeping your customer happy is the aim of your services. However, It is very difficult / nearly impossible for you to put a filter and check every single use case of attack and/or malfunction.

Anomaly detection graph

Our approach

Our approach is based on machine learning in order to train a neuronal network and record his natural traffic patterns. When something unusual/unexpected happens, we can report the issue quickly and tell you where you would need to intervene.

90% of the work is to understand the data, meta data and the enterprise logic. this is the First step.

Many machine learning models already are on the market, however, In order to get a fine tuned solution an complex mathematical understanding is needed in order to create an effective custom IoT Traffic Model.

The second step is to train your neuronal network with two datasets. One dataset to feed the evolutive,learning algorithm and another dataset in order to test the effeciveness of your model.

When these steps have given you a result that you are happy with we pass on to the next and final step the third step consist in building the infastructure so you can follow up your real time datastream by monitoring, alerts and reportings.

Data Exploration with Phileas