Selfish Response to Epidemic Propagation

An epidemic spreading in a network calls for a decision on the part of the network members: They should decide whether to protect themselves or not. Their decision depends on the trade-off between their perceived risk of being infected and the cost of being protected. The network members can make decisions repeatedly, based on information that they receive about the changing infection level in the network. We study the equilibrium states reached by a network whose members increase (resp. decrease) their security deployment when learning that the network infection is widespread (resp. limited). Our main finding is that the equilibrium level of infection increases as the learning rate of the members increases. We confirm this result in three scenarios for the behavior of the members: strictly rational cost minimizers, not strictly rational, and strictly rational but split into two response classes. In the first two cases, we completely characterize the stability and the domains of attraction of the equilibrium points, even though the first case leads to a differential inclusion. We validate our conclusions with simulations on human mobility traces.Comment: 19 pages, 5 figures, submitted to the IEEE Transactions on Automatic Contro

Leveraging Expert Models for Training Deep Neural Networks in Scarce Data Domains: Application to Offline Handwritten Signature Verification

This paper introduces a novel approach to leverage the knowledge of existing expert models for training new Convolutional Neural Networks, on domains where task-specific data are limited or unavailable. The presented scheme is applied in offline handwritten signature verification (OffSV) which, akin to other biometric applications, suffers from inherent data limitations due to regulatory restrictions. The proposed Student-Teacher (S-T) configuration utilizes feature-based knowledge distillation (FKD), combining graph-based similarity for local activations with global similarity measures to supervise student's training, using only handwritten text data. Remarkably, the models trained using this technique exhibit comparable, if not superior, performance to the teacher model across three popular signature datasets. More importantly, these results are attained without employing any signatures during the feature extraction training process. This study demonstrates the efficacy of leveraging existing expert models to overcome data scarcity challenges in OffSV and potentially other related domains

Linear Iterations on Ordered Semirings for Trust Metric Computation and Attack Resiliency Evaluation

Within the realm of network security, we interpret the concept of trust as a relation among entities that participate in various protocols. Trust relations are based on evidence created by the previous interactions of entities within a protocol. In this work, we are focusing on the evaluation of trust evidence in Ad Hoc Networks. Because of the dynamic nature of Ad Hoc Networks, trust evidence may be uncertain and incomplete. Also, no pre-established infrastructure can be assumed. The evaluation process is modelled as a path problem on a directed graph, where nodes represent entities, and edges represent trust relations. We develop a novel formulation of trust computation as linear iterations on ordered semirings. Using the theory of semirings, we analyze several key problems on the performance of trust algorithms. We also analyze the resilience to attacks of the resulting schemes

An RFP dataset for Real, Fake, and Partially fake audio detection

Recent advances in deep learning have enabled the creation of natural-sounding synthesised speech. However, attackers have also utilised these tech-nologies to conduct attacks such as phishing. Numerous public datasets have been created to facilitate the development of effective detection models. How-ever, available datasets contain only entirely fake audio; therefore, detection models may miss attacks that replace a short section of the real audio with fake audio. In recognition of this problem, the current paper presents the RFP da-taset, which comprises five distinct audio types: partial fake (PF), audio with noise, voice conversion (VC), text-to-speech (TTS), and real. The data are then used to evaluate several detection models, revealing that the available detec-tion models incur a markedly higher equal error rate (EER) when detecting PF audio instead of entirely fake audio. The lowest EER recorded was 25.42%. Therefore, we believe that creators of detection models must seriously consid-er using datasets like RFP that include PF and other types of fake audio

A Testbed for Comparing Trust Computation Algorithms

Trust is the expectation of a person about another person’s behavior. Trust is important for many security related decisions about, e.g., granting or revoking privileges, controlling access to sensitive resources and information, or evaluating intelligence gathered from multiple sources. More often than not, the issue is complicated even further because the person making the decision has no direct trust relationship with every single subject whose trustworthiness needs to be evaluated. So, the decision maker needs to rely on recommendations by others, and then somehow aggregate the trust related information that is collected. In this work we provide an algebraic framework in which we can describe multiple ways that trust related information can be aggregated to form a single value. We show the similarities and differences that the various so called trust computation algorithms have, and associate these with the algebraic properties of the framework that we consider

Automating GDPR compliance verification for cloud-hosted services

Cloud-hosted business processes require access to customer data to complete a transaction, to improve a customer’s on-line experience or provide useful product recommendations. However, privacy concerns associated with the use of this data have led to legal regulations that impose restrictions on how such data is requested or processed by an on-line service, with large penalties for violating these restrictions, e.g. the European General Data Protection Regulation (GDPR). We propose a framework for helping cloud-hosted services automate GDPR compliance checking. The framework comprises three steps: represent data flow in business processes with an appropriate abstraction (timed transition systems), formalise GDPR rules and obligations and incorporate them into the same abstraction, and implement the abstraction in a model checking tool (Uppaal) in order to automatically verify compliance of business process activities with GDPR. We demonstrate the approach using a cloud-based purchase order system

Coalition Formation in MANETs

Wireless ad-hoc networks rely on the cooperation of participating nodes for almost all their functions. However, due to resource constraints, nodes are generally selfish and try to maximize their own benefit when participating in the network. Therefore, it is important to study mechanisms which can be used as incentives to form coalitions inside the network. In this paper, we study coalition formation based on game theory, especially cooperative game theory. First, the dynamics of coalition formation proceeds via pairwise bargaining. We show that the size of the maximum coalition is a decreasing function of the cost for establishing a link. After the coalition formation process reaches the steady state, we are interested in the stability of coalitions. We prove that coalitions are stable in terms of both pairwise stability and coalitional stability