arXiv subjects

C. Lara

Publications and source records attributed to C. Lara.

3 recordsLinked to original sources

arXiv2020

A multi-chord stellar occultation by the large trans-Neptunian object (174567) Varda

We present results from the first recorded stellar occultation by the large trans-Neptunian object (174567) Varda that was observed on September 10$^{\rm th}$, 2018. Varda belongs to the high-inclination dynamically excited population, and has a satellite, Ilmar\"e, which is half the size of Varda. We determine the size and albedo of Varda and constrain its 3D shape and density. Thirteen different sites in the USA monitored the event, five of which detected an occultation by the main body. A best-fitting ellipse to the occultation chords provides the instantaneous limb of the body, from which the geometric albedo is computed. The size and shape of Varda are evaluated, and its bulk density is constrained, using Varda's mass known from previous works. The best-fitting elliptical limb has semi-major (equatorial) axis of $(383 \pm 3)$km and an apparent oblateness $0.066\pm0.047$ corresponding to an apparent area-equivalent radius $R'_{\rm equiv}= (370\pm7)$km and geometric albedo $p_v=0.099\pm 0.002 $ assuming a visual absolute magnitude $H_V=3.81\pm0.01$. Using three possible rotational periods for the body (4.76h, 5.91h, and 7.87h), we derive corresponding MacLaurin solutions. Furthermore, given the low-amplitude ($0.06\pm0.01$) mag of the single-peaked rotational light-curve for the aforementioned periods, we consider the double periods. For the 5.91h period (the most probable) and its double (11.82h), we find bulk densities and true oblateness of $\rho=(1.78\pm0.06)$ g cm$^{-3}$, $\epsilon=0.235\pm0.050$ and $\rho=(1.23\pm0.04)$ g cm$^{-3}$, $\epsilon=0.080\pm0.049$. However, it must be noted that the other solutions cannot be excluded just yet.

astro-ph.EP↗

arXiv2018

Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing

Grid computing systems require innovative methods and tools to identify cybersecurity incidents and perform autonomous actions i.e. without administrator intervention. They also require methods to isolate and trace job payload activity in order to protect users and find evidence of malicious behavior. We introduce an integrated approach of security monitoring via Security by Isolation with Linux Containers and Deep Learning methods for the analysis of real time data in Grid jobs running inside virtualized High-Throughput Computing infrastructure in order to detect and prevent intrusions. A dataset for malware detection in Grid computing is described. We show in addition the utilization of generative methods with Recurrent Neural Networks to improve the collected dataset. We present Arhuaco, a prototype implementation of the proposed methods. We empirically study the performance of our technique. The results show that Arhuaco outperforms other methods used in Intrusion Detection Systems for Grid Computing. The study is carried out in the ALICE Collaboration Grid, part of the Worldwide LHC Computing Grid.

cs.DC↗

arXiv2017

A Security Monitoring Framework For Virtualization Based HEP Infrastructures

High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware. This malware was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.

cs.DC↗