7 research outputs found
The Meeting of Acquaintances: A Cost-efficient Authentication Scheme for Light-weight Objects with Transient Trust Level and Plurality Approach
Wireless sensor networks consist of a large number of distributed sensor
nodes so that potential risks are becoming more and more unpredictable. The new
entrants pose the potential risks when they move into the secure zone. To build
a door wall that provides safe and secured for the system, many recent research
works applied the initial authentication process. However, the majority of the
previous articles only focused on the Central Authority (CA) since this leads
to an increase in the computation cost and energy consumption for the specific
cases on the Internet of Things (IoT). Hence, in this article, we will lessen
the importance of these third parties through proposing an enhanced
authentication mechanism that includes key management and evaluation based on
the past interactions to assist the objects joining a secured area without any
nearby CA. We refer to a mobility dataset from CRAWDAD collected at the
University Politehnica of Bucharest and rebuild into a new random dataset
larger than the old one. The new one is an input for a simulated authenticating
algorithm to observe the communication cost and resource usage of devices. Our
proposal helps the authenticating flexible, being strict with unknown devices
into the secured zone. The threshold of maximum friends can modify based on the
optimization of the symmetric-key algorithm to diminish communication costs
(our experimental results compare to previous schemes less than 2000 bits) and
raise flexibility in resource-constrained environments.Comment: 27 page
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
The superconducting LHC magnets are coupled with an electronic monitoring
system which records and analyses voltage time series reflecting their
performance. A currently used system is based on a range of preprogrammed
triggers which launches protection procedures when a misbehavior of the magnets
is detected. All the procedures used in the protection equipment were designed
and implemented according to known working scenarios of the system and are
updated and monitored by human operators.
This paper proposes a novel approach to monitoring and fault protection of
the Large Hadron Collider (LHC) superconducting magnets which employs
state-of-the-art Deep Learning algorithms. Consequently, the authors of the
paper decided to examine the performance of LSTM recurrent neural networks for
modeling of voltage time series of the magnets. In order to address this
challenging task different network architectures and hyper-parameters were used
to achieve the best possible performance of the solution. The regression
results were measured in terms of RMSE for different number of future steps and
history length taken into account for the prediction. The best result of
RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal
layer and 16 steps history buffer
Formalizing and safeguarding blockchain-based BlockVoke protocol as an ACME extension for fast certificate revocation
Certificates are integral to the security of today’s Internet. Protocols like BlockVoke allow secure, timely and efficient revocation of certificates that need to be invalidated. ACME, a scheme used by the non-profit Let’s Encrypt Certificate Authority to handle most parts of the certificate lifecycle, allows automatic and seamless certificate issuance. In this work, we bring together both protocols by describing and formalizing an extension of the ACME protocol to support BlockVoke, combining the benefits of ACME’s certificate lifecycle management and BlockVoke’s timely and secure revocations. We then formally verify this extension through formal methods such as Colored Petri Nets (CPNs) and conduct a risk and threat analysis of the ACME/BlockVoke extension using the ISSRM domain model. Identified risks and threats are mitigated to secure our novel extension. Furthermore, a proof-of-concept implementation of the ACME/BlockVoke extension is provided, bridging the gap towards deployment in the real world
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
This paper focuses on an examination of an applicability of Recurrent Neural
Network models for detecting anomalous behavior of the CERN superconducting
magnets. In order to conduct the experiments, the authors designed and
implemented an adaptive signal quantization algorithm and a custom GRU-based
detector and developed a method for the detector parameters selection. Three
different datasets were used for testing the detector. Two artificially
generated datasets were used to assess the raw performance of the system
whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets
was intended for real-life experiments and model training. Several different
setups of the developed anomaly detection system were evaluated and compared
with state-of-the-art OC-SVM reference model operating on the same data. The
OC-SVM model was equipped with a rich set of feature extractors accounting for
a range of the input signal properties. It was determined in the course of the
experiments that the detector, along with its supporting design methodology,
reaches F1 equal or very close to 1 for almost all test sets. Due to the
profile of the data, the best_length setup of the detector turned out to
perform the best among all five tested configuration schemes of the detection
system. The quantization parameters have the biggest impact on the overall
performance of the detector with the best values of input/output grid equal to
16 and 8, respectively. The proposed solution of the detection significantly
outperformed OC-SVM-based detector in most of the cases, with much more stable
performance across all the datasets.Comment: Related to arXiv:1702.0083
Estudio de modelos de privacidad de datos
El presente trabajo surge como una investigación motivada por la necesidad de proteger la privacidad de los usuarios de sistemas en contextos de análisis estadÃstico, inteligencia artificial y publicación de datos. Para ello se ha llevado a cabo un estudio del estado del arte y se han explorado técnicas de privatización de datos basadas en Privacidad Diferencial.Agencia Nacional de Investigación e InnovaciónICT4
PenChain: A Blockchain-Based Platform for Penalty-Aware Service Provisioning
Service provisioning is of paramount importance as we are now heading towards a world of integrated services giving rise to the next generation of service ecosystems. The huge number of service offerings that will be available to customers in future scenarios require a novel approach to service registry and discovery that allows customers to choose the offerings that best match their preferences. One way to achieve this is to introduce the provider’s reputation, i.e., a quality indicator of the provisioned service, as an additional search criterion. Now, with blockchain technology in our hands, automated regulation of service-level agreements (SLAs) that capture mutual agreements between all involved parties has regained momentum. In this article, we report on our full-fledged work on the conception, design, and construction of a platform for SLA-minded service provisioning called PenChain. With our work, we demonstrate that penalty-aware SLAs of general services–if represented in machine-readable logic and assisted by distributed ledger technology–are programmatically enforceable. We devise algorithms for ranking services in a search result taking into account the digitized values of the SLAs. We offer two scenario-based evaluations of PenChain in the field of precision agriculture and in the domain of automotive manufacturing. Furthermore, we examine the scalability and data security of PenChain for precision agriculture