The explanatory and predictive power of social simulations is more and more connected with the development of models accounting for the complexity of real (inter-individual and intra-individual) social dynamics. From this perspective, a promising research path is complementing very simple models, more suitable to illuminate core dynamics of social phenomena, with increasingly more complex and empirically grounded simulations (big data-driven models, higher number of agents, more detailed and realistic description of cognitive and communication mechanisms underlying individual and group behaviors). The choice has two strictly intertwined effects: not only a different modeling approach, but also the need for more powerful tools.
We present a quantum-public-key identification protocol and show that it is secure against a computationally-unbounded adversary. This demonstrates for the first time that unconditionally-secure and reusable public-key authentication is possible in principle with (pure-state) public keys.
A simplified version of the kernel language SCEL, that we call SCELlight, is introduced as a formalism for programming and verifying properties of so-called cyber-physical systems consisting of software-intensive ensembles of components, featuring complex intercommunications and interactions with humans and other systems. In order to validate the amenability of the language for verification purposes, we provide a translation of SCELlight specifications into Promela. We test the feasibility of the approach by formally specifying an application scenario, consisting of a collection of components offering a variety of services meeting different quality levels, and by using SPIN to verify that some desired behaviors are guaranteed
Part 9: Malicious Behavior and FraudInternational audienceWe propose a semi-supervised online banking fraud analysis and decision support approach. During a training phase, it builds a profile for each customer based on past transactions. At runtime, it supports the analyst by ranking unforeseen transactions that deviate from the learned profiles. It uses methods whose output has a immediate statistical meaning that provide the analyst with an easy-to-understand model of each customer’s spending habits. First, we quantify the anomaly of each transaction with respect to the customer historical profile. Second, we find global clusters of customers with similar spending habits. Third, we use a temporal threshold system that measures the anomaly of the current spending pattern of each customer, with respect to his or her past spending behavior. As a result, we mitigate the undertraining due to the lack of historical data for building of well-trained profiles (of fresh users), and the users that change their (spending) habits over time. Our evaluation on real-world data shows that our approach correctly ranks complex frauds as “top priority”
Part 3: Security AnalysisInternational audienceWe put forward a model of action-based randomization mechanisms to analyse quantitative information flow (qif) under generic leakage functions, and under possibly adaptive adversaries. This model subsumes many of the qif models proposed so far. Our main contributions include the following: (1) we identify mild general conditions on the leakage function under which it is possible to derive general and significant results on adaptive qif; (2) we contrast the efficiency of adaptive and non-adaptive strategies, showing that the latter are as efficient as the former in terms of length up to an expansion factor bounded by the number of available actions; (3) we show that the maximum information leakage over strategies, given a finite time horizon, can be expressed in terms of a Bellman equation. This can be used to compute an optimal finite strategy recursively, by resorting to standard methods like backward induction
Clustering algorithms are largely adopted in security applications as a vehicle to detect malicious activities, although few attention has been paid on preventing deliberate attacks from subverting the clustering process itself. Recent work has introduced a methodology for the security analysis of data clustering in adversarial settings, aimed to identify potential attacks against clustering algorithms and to evaluate their impact. The authors have shown that single-linkage hierarchical clustering can be severely affected by the presence of a very small fraction of carefully-crafted poisoning attacks into the input data, highlighting that the clustering algorithm may be itself the weakest link in a security system. In this paper, we extend this analysis to the case of complete-linkage hierarchical clustering by devising an ad hoc poisoning attack. We verify its effectiveness on artificial data and on application examples related to the clustering of malware and handwritten digits.
Part 2: Track B: Logic, Semantics, Specification and VerificationInternational audienceThe interplay between process behaviour and spatial aspects of computation has become more and more relevant in Computer Science, especially in the field of collective adaptive systems, but also, more generally, when dealing with systems distributed in physical space. Traditional verification techniques are well suited to analyse the temporal evolution of programs; properties of space are typically not explicitly taken into account. We propose a methodology to verify properties depending upon physical space. We define an appropriate logic, stemming from the tradition of topological interpretations of modal logics, dating back to earlier logicians such as Tarski, where modalities describe neighbourhood. We lift the topological definitions to a more general setting, also encompassing discrete, graph-based structures. We further extend the framework with a spatial until operator, and define an efficient model checking procedure, implemented in a proof-of-concept tool
Part 6: Engineering and Implementation of Collaborative NetworksInternational audienceCollaboration is a key factor of sustainable growth across territories and industrial sectors. Tourism, one of the largest industries in the world, has been subject to strongest innovation in the last years. Main reasons of this reside both in the availability of new ICTs - Information and Communication Technologies - and organizational models, which directly connect tourists among them and with service providers, and in the always more personalized supply of tourism experience. Tourism destinations can benefit of such innovations if they are able to reorganize the territorial tourism offer around different pattern of collaboration in order to give 2.0 tourists opportunities to live an augmented tourism experience. This paper deals with the possible forms of collaborative networks that can rise within a destination with a focus on relationships between services delivered by the Tourism Destination and the requests of services at the different phases of the tourist 2.0 lifecycle
Part 7: Cyber-Physical SystemsInternational audienceA resource-efficient Europe is a pillar of the EU 2020 program which aims at smart, sustainable, inclusive growth. The diffusion of smart networked environments, wherein humans, intelligent agents and devices collaborate, is fundamental for achieving energy-efficiency in buildings. In this context, this paper deals with the topic of Smart Home Environments (SHEs), where users can exploit multimedia services to interact with heterogeneous and interconnected smart appliances in order to save energy, reduce costs and improve users’ comfort and safety. In particular, we propose an interoperable architectural framework and a related knowledge-based management model, associated with a specific forecasting model, for monitoring and managing energy consumption in SHEs