Term-based composition of security protocols

In the context of security protocol parallel composition, where messages belonging to different protocols can intersect each other, we introduce a new paradigm: term-based composition (i.e. the composition of message components also known as terms). First, we create a protocol specification model by extending the original strand spaces. Then, we provide a term composition algorithm based on which new terms can be constructed. To ensure that security properties are maintained, we introduce the concept of term connections to express the existing connections between terms and encryption contexts. We illustrate the proposed composition process by using two existing protocols.Comment: 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, May 2008, pp. 233-238, ISBN 978-1-4244-2576-

Cyber-Physical Attacks: The Role of Network Parameters

The fact that modern Networked Industrial Control Systems (NICS) depend on Information and Communications Technologies (ICT) is well known. Although many studies have focused on the security of NICS, today we still lack a proper understanding of the impact that network parameters, e.g. network delays, packet losses, background traffic, and network design decisions, have on cyber attacks targeting NICS. In this paper we investigate the impact of network parameters on cyber attacks targeting industrial processes. Our analysis is based on the Tennessee-Eastman chemical process and proves that network parameters have a limited effect on remote cyber attacks.JRC.G.6-Security technology assessmen

Efficient Behavior Prediction Based on User Events

In 2020 we have witnessed the dawn of machine learning enabled user experience. Now we can predict how users will use an application. Research progressed beyond recommendations, and we are ready to predict user events. Whenever a human interacts with a system, user events are dispatched. They can be as simple as a mouse click on a menu item or more complex, such as buying a product from an eCommerce site. Collaborative filtering (CF) has proven to be an excellent approach to predict events. Because each user can generate many events, this inevitably leads to a vast number of events in a dataset. Unfortunately, the operation time of CF increases exponentially with the increase of data-points. This paper presents a generalized approach to reduce the dataset’s size without compromising prediction accuracy. Our solution transformed a dataset containing over 20 million user events (20,692,840 rows) into a sparse matrix in about 7 minutes (434.08 s). We have used this matrix to train a neural network to accurately predict user events