Insecure SSL Remote Desktop Protocol Traffic: snooPDR Development

poster abstractAbstract: The goal of this project was to show how vulnerable SSL-secured Remote Desktop Protocol communication using RSA is. This project will develop a method to capture authentication packets of an RDP session and decrypt the SSL key used. A secondary goal is to develop a method to replay the authentication packets with the RDP server after the snooped session has ended. The motivation of this project is to demonstrate the insecurity of RSA-encrypted SSL encryption in Remote Desktop Protocol connections used by many network administrators. This project will build a Linux installation which can capture Remote Desktop Protocol packets and develop a method to decrypt the confidential communication between the client and the server. Database Security Techniques used in this project will include: access security in authentication to the operating system and encryption of data at rest because Linux hashes passwords in the users database of the operating system. This project will be exploiting access control to the RDP server. The secondary goal of this project will be to authorize an untrusted user to access confidential data assets. The expected result of this project is to successfully capture and monitor the packets associated with authentication to an RDP server and secondarily to be able to successfully masquerade as the previously authenticated user. Evaluations will include the ability to successfully capture 10 RDP sessions, decrypt them, and store the packet information into an SQL database. Results include the ability to insert packet data into a database, capture encrypted traffic and decrypt traffic if the private key is known

The discrete energy method in numerical relativity: Towards long-term stability

The energy method can be used to identify well-posed initial boundary value problems for quasi-linear, symmetric hyperbolic partial differential equations with maximally dissipative boundary conditions. A similar analysis of the discrete system can be used to construct stable finite difference equations for these problems at the linear level. In this paper we apply these techniques to some test problems commonly used in numerical relativity and observe that while we obtain convergent schemes, fast growing modes, or ``artificial instabilities,'' contaminate the solution. We find that these growing modes can partially arise from the lack of a Leibnitz rule for discrete derivatives and discuss ways to limit this spurious growth.Comment: 18 pages, 22 figure

A scalable elliptic solver with task-based parallelism for the SpECTRE numerical relativity code

Elliptic partial differential equations must be solved numerically for many problems in numerical relativity, such as initial data for every simulation of merging black holes and neutron stars. Existing elliptic solvers can take multiple days to solve these problems at high resolution and when matter is involved, because they are either hard to parallelize or require a large amount of computational resources. Here we present a new solver for linear and non-linear elliptic problems that is designed to scale with resolution and to parallelize on computing clusters. To achieve this we employ a discontinuous Galerkin discretization, an iterative multigrid-Schwarz preconditioned Newton-Krylov algorithm, and a task-based parallelism paradigm. To accelerate convergence of the elliptic solver we have developed novel subdomain-preconditioning techniques. We find that our multigrid-Schwarz preconditioned elliptic solves achieve iteration counts that are independent of resolution, and our task-based parallel programs scale over 200 million degrees of freedom to at least a few thousand cores. Our new code solves a classic black-hole binary initial-data problem faster than the spectral code SpEC when distributed to only eight cores, and in a fraction of the time on more cores. It is publicly accessible in the next-generation SpECTRE numerical relativity code. Our results pave the way for highly-parallel elliptic solves in numerical relativity and beyond.Comment: 24 pages, 18 figures. Results are reproducible with the ancillary input file

Replicability, Robustness, and Reproducibility in Psychological Science

Replication—an important, uncommon, and misunderstood practice—is gaining appreciation in psychology. Achieving replicability is important for making research progress. If findings are not replicable, then prediction and theory development are stifled. If findings are replicable, then interrogation of their meaning and validity can advance knowledge. Assessing replicability can be productive for generating and testing hypotheses by actively confronting current understandings to identify weaknesses and spur innovation. For psychology, the 2010s might be characterized as a decade of active confrontation. Systematic and multi-site replication projects assessed current understandings and observed surprising failures to replicate many published findings. Replication efforts highlighted sociocultural challenges such as disincentives to conduct replications and a tendency to frame replication as a personal attack rather than a healthy scientific practice, and they raised awareness that replication contributes to self-correction. Nevertheless, innovation in doing and understanding replication and its cousins, reproducibility and robustness, has positioned psychology to improve research practices and accelerate progress

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Cross-Sector Review of Drivers and Available 3Rs Approaches for Acute Systemic Toxicity Testing

Acute systemic toxicity studies are carried out in many sectors in which synthetic chemicals are manufactured or used and are among the most criticized of all toxicology tests on both scientific and ethical grounds. A review of the drivers for acute toxicity testing within the pharmaceutical industry led to a paradigm shift whereby in vivo acute toxicity data are no longer routinely required in advance of human clinical trials. Based on this experience, the following review was undertaken to identify (1) regulatory and scientific drivers for acute toxicity testing in other industrial sectors, (2) activities aimed at replacing, reducing, or refining the use of animals, and (3) recommendations for future work in this area

Measuring Regional Innovativeness - A Methodological Discussion and an Application to One German Industry

The regional or national innovation performance has been repeatedly measured in the literature; but it has so far not been discussed what this means, especially in relation to a region. What is the contribution of a region to innovation output? The usual approaches implicitly assume that higher innovation outputs per inhabitant, employee, or R&D employee can be assigned to a region. We argue that more insights are gained if we distinguish between various mechanisms that influence the innovation activities in a region. Different analyses need to be conducted, using different variables and including different local factors. Furthermore, we see no justification for using a linear dependence of innovation activity on the number of inhabitants or employees as a benchmark for performance. We use a method that takes into account these arguments and apply it to the Electrics & Electronics industry in Germany

Validation of an enzyme-linked immunosorbent assay that detects Histoplasma capsulatum antigenuria in colombian patients with AIDS for diagnosis and follow-up during therapy

We validated an antigen capture enzyme-linked immunosorbent assay (ELISA) in Colombian persons with AIDS and proven histoplasmosis and evaluated the correlation between antigenuria and clinical improvement during follow-up. The sensitivity of the Histoplasma capsulatum ELISA was 86%, and the overall specificity was 94%. The antigen test successfully monitored the response to therapy. Copyright © 2014, American Society for Microbiology. All Rights Reserved

Validation of an enzyme-linked immunosorbent assay that detects Histoplasma capsulatum antigenuria in colombian patients with AIDS for diagnosis and follow-up during therapy

We validated an antigen capture enzyme-linked immunosorbent assay (ELISA) in Colombian persons with AIDS and proven histoplasmosis and evaluated the correlation between antigenuria and clinical improvement during follow-up. The sensitivity of the Histoplasma capsulatum ELISA was 86%, and the overall specificity was 94%. The antigen test successfully monitored the response to therapy. Copyright © 2014, American Society for Microbiology. All Rights Reserved