In the proposed work is performed a text classification for a chatbot application used by a company working in assistance services of automatic warehouses. industries. Specifically, text mining technique is adopted for the classification of questions and answers. Business Process Modeling Notation (BPMN) models describe the passage from “AS-IS” to “TO BE” in the context of the analyzed industry, by focusing the attention mainly on customer and technical support services where chatbot is adopted. A two-step process model is used to connect technological improvements and relationship marketing in chatbot assistance: the first step provides the hierarchical clustering able to classify questions and answers through Latent Dirichlet Allocation -LDA- algorithm, and the second one executes the Tag Cloud representing the visual representation of more frequent words contained in the experimental dataset. Tag cloud is used to show the critical issues that customers find in the usage of the proposed service. By considering an initial dataset, 24 hierarchical clusters are found representing the preliminary combination of the couple’s question-answer. The proposed approach is suitable to automatically construct a combination of chatbot questions and appropriate answers in intelligent systems.

Alessandro Massaro: Professor Alessandro Massaro (ING/INF/01, FIS/01,FIS/03) carried out scientific research at the Polytechnic University of Marche, at CNR, and at Italian Institute of Technology (IIT) as Team Leader by activating laboratories for nanocomposite sensors for industrial robotics. He is in MIUR register as scientific expert in competitive Industrial Research and social development, and he is currently head of the Research and Development section and scientific director of MIUR Research Institute Dyrecta Lab Srl. Member of the International Scientific Committee of Measurers IMEKO and IEEE Senior member, recently received an award from the National Council of Engineers as Best Engineer of Italy 2018 (Top Young Engineer 2018).

This paper describes a prototype cross platform based on intelligent switching of Virtual Private Network (VPN) communications by means of artificial intelligence algorithms able to identify and classify attack risks in self-learning mode by analysing the traffic logs of the system. The platform is also suitable for disaster recovery, data migration and ensures virtualization of communications between nodes in case of risk detection. In order to test the models and evaluate the accuracy of the AI algorithms for risk detection and classification, a number of cyberattack scenario have been simulated. The proposed platform implements Cassandra Big Data system interfacing with supernodes enabling data migration, security and disaster recovery. By comparing the performance of different AI algorithms, the results show that a XGBoost-based algorithm is the most efficient and accurate method for cyberattacks prevention, showing a remarkable ability of classifying and identifying characteristic patterns of the most representative traffic log variables. The research work has been carried out within the framework of a research industry project.