17,638 research outputs found
Actor Network Procedures as Psi-calculi for Security Ceremonies
The actor network procedures of Pavlovic and Meadows are a recent graphical
formalism developed for describing security ceremonies and for reasoning about
their security properties. The present work studies the relations of the actor
network procedures (ANP) to the recent psi-calculi framework. Psi-calculi is a
parametric formalism where calculi like spi- or applied-pi are found as
instances. Psi-calculi are operational and largely non-graphical, but have
strong foundation based on the theory of nominal sets and process algebras. One
purpose of the present work is to give a semantics to ANP through psi-calculi.
Another aim was to give a graphical language for a psi-calculus instance for
security ceremonies. At the same time, this work provides more insight into the
details of the ANPs formalization and the graphical representation.Comment: In Proceedings GraMSec 2014, arXiv:1404.163
Interpretable Probabilistic Password Strength Meters via Deep Learning
Probabilistic password strength meters have been proved to be the most
accurate tools to measure password strength. Unfortunately, by construction,
they are limited to solely produce an opaque security estimation that fails to
fully support the user during the password composition. In the present work, we
move the first steps towards cracking the intelligibility barrier of this
compelling class of meters. We show that probabilistic password meters
inherently own the capability of describing the latent relation occurring
between password strength and password structure. In our approach, the security
contribution of each character composing a password is disentangled and used to
provide explicit fine-grained feedback for the user. Furthermore, unlike
existing heuristic constructions, our method is free from any human bias, and,
more importantly, its feedback has a clear probabilistic interpretation. In our
contribution: (1) we formulate the theoretical foundations of interpretable
probabilistic password strength meters; (2) we describe how they can be
implemented via an efficient and lightweight deep learning framework suitable
for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the
25th European Symposium on Research in Computer Security (ESORICS) 202
Secure Cloud-Edge Deployments, with Trust
Assessing the security level of IoT applications to be deployed to
heterogeneous Cloud-Edge infrastructures operated by different providers is a
non-trivial task. In this article, we present a methodology that permits to
express security requirements for IoT applications, as well as infrastructure
security capabilities, in a simple and declarative manner, and to automatically
obtain an explainable assessment of the security level of the possible
application deployments. The methodology also considers the impact of trust
relations among different stakeholders using or managing Cloud-Edge
infrastructures. A lifelike example is used to showcase the prototyped
implementation of the methodology
Exact Inference Techniques for the Analysis of Bayesian Attack Graphs
Attack graphs are a powerful tool for security risk assessment by analysing
network vulnerabilities and the paths attackers can use to compromise network
resources. The uncertainty about the attacker's behaviour makes Bayesian
networks suitable to model attack graphs to perform static and dynamic
analysis. Previous approaches have focused on the formalization of attack
graphs into a Bayesian model rather than proposing mechanisms for their
analysis. In this paper we propose to use efficient algorithms to make exact
inference in Bayesian attack graphs, enabling the static and dynamic network
risk assessments. To support the validity of our approach we have performed an
extensive experimental evaluation on synthetic Bayesian attack graphs with
different topologies, showing the computational advantages in terms of time and
memory use of the proposed techniques when compared to existing approaches.Comment: 14 pages, 15 figure
A multi-INT semantic reasoning framework for intelligence analysis support
Lockheed Martin Corp. has funded research to generate a framework
and methodology for developing semantic reasoning applications to support the
discipline oflntelligence Analysis. This chapter outlines that framework, discusses
how it may be used to advance the information sharing and integrated analytic
needs of the Intelligence Community, and suggests a system I software
architecture for such applications
Probabilistic Relational Model Benchmark Generation
The validation of any database mining methodology goes through an evaluation
process where benchmarks availability is essential. In this paper, we aim to
randomly generate relational database benchmarks that allow to check
probabilistic dependencies among the attributes. We are particularly interested
in Probabilistic Relational Models (PRMs), which extend Bayesian Networks (BNs)
to a relational data mining context and enable effective and robust reasoning
over relational data. Even though a panoply of works have focused, separately ,
on the generation of random Bayesian networks and relational databases, no work
has been identified for PRMs on that track. This paper provides an algorithmic
approach for generating random PRMs from scratch to fill this gap. The proposed
method allows to generate PRMs as well as synthetic relational data from a
randomly generated relational schema and a random set of probabilistic
dependencies. This can be of interest not only for machine learning researchers
to evaluate their proposals in a common framework, but also for databases
designers to evaluate the effectiveness of the components of a database
management system
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
- …