Abstract: A computer-implemented method of detecting abnormal events in a telecommunication system can include a training phase and a production phase. The training phase can include recording data events in a log file during the execution of the system and creating a standard for the data events representing a properly working environment of the telecommunication system by applying a training procedure. The production phase can include receiving at least one new data event and determining whether the at least one new data event is a normal event that matches the standard or not. If the at least one new data event matches the standard, it can be used for updating the standard. If the at least one new data event does not match the standard, it can be classified and reported as at least one an abnormal event.

Abstract: A method for secure validation of machine learning models and parallel validation data using homomorphic encryption can include providing, by a provider, a machine learning model and providing, by a user, validation data; encrypting, by the provider, the machine learning model; sending, by the provider, a public encryption parameter to the user; selecting, by the user and provider, a unifying encoding method; encrypting, by the user, the validation data; sending, by the user, the encrypted validation data; processing, the encrypted validation data with the encrypted machine learning model; providing encrypted results of said processing to the provider and the user; and decrypting the results and evaluating whether the performance of the machine learning model is satisfactory with the given valuation data of the user.

Abstract: A computer-implemented method of detecting abnormal events in a telecommunication system can include a training phase and a production phase. The training phase can include recording data events in a log file during the execution of the system and creating a standard for the data events representing a properly working environment of the telecommunication system by applying a training procedure. The production phase can include receiving at least one new data event and determining whether the at least one new data event is a normal event that matches the standard or not. If the at least one new data event matches the standard, it can be used for updating the standard. If the at least one new data event does not match the standard, it can be classified and reported as at least one an abnormal event.