Abstract
The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution-embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods-are already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: the integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machinery and its evolution towards a full proactive maintenance system.

Abstract
Networking equipment that connects households to an operator network, such as home gateways and routers, are major victims of cyber-attacks, being exposed to a number of threats, from misappropriation of user accounts by malicious agents to access to personal information and data, threatening users’ privacy and security. The exposure surface to threats is even wider when the growing ecosystem of Internet-of-Things devices is considered. Thus, it is beneficial for the operator and customer that a security service is provided to protect this ecosystem. The service should be tailored to the particular needs and Internet usage profile of the customer network. For this purpose, Machine Learning methods can be explored to learn typical behaviours and identify anomalies. In this paper, we present preliminary insights into the architecture and mechanisms of a security service offered by an Internet Service Provider. We focus on Distributed Denial-of-Service kind of attacks and define the system requirements. Finally, we analyse the trade-offs of distributing the service between operator equipment deployed at the customer premises and cloud-hosted servers.

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