22 Search Results

Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Producing vehicular CAN data with a variety of intrusions is a difficult task for most researchers as it requires expensive assets and deep expertise. To illuminate this task, we introduce the first comprehensive guide to the existing open CAN intrusion detection system (IDS) datasets. We categorize attacks on CANs including fabrication (adding frames, e.g., flooding or targeting and ID), suspension (removing an ID’s frames),more » and masquerade attacks (spoofed frames sent in lieu of suspended ones). We provide a quality analysis of each dataset; an enumeration of each datasets’ attacks, benefits, and drawbacks; categorization as real vs. simulated CAN data and real vs. simulated attacks; whether the data is raw CAN data or signal-translated; number of vehicles/CANs; quantity in terms of time; and finally a suggested use case of each dataset. State-of-the-art public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, lacking fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but is missing a corresponding “raw” binary version. This issue pigeon-holes CAN IDS research into testing on limited and often inappropriate data (usually with attacks that are too easily detectable to truly test the method). The scarcity of appropriate data has stymied comparability and reproducibility of results for researchers. As our primary contribution, we present the Real ORNL Automotive Dynamometer (ROAD) CAN IDS dataset, consisting of over 3.5 hours of one vehicle’s CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real (i.e. non-simulated) fuzzing, fabrication, unique advanced attacks, and simulated masquerade attacks. To facilitate a benchmark for CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS research field.« less

Prokaryotes, the earliest forms of life on Earth, play crucial roles in global biogeochemical processes in virtually all ecosystems. The ever-increasing amount of prokaryotic genome sequencing data provides a wealth of information to examine fundamental and applied questions through systematic genome comparison. Genomic features, such as genome size and GC content, and taxonomy-centric genomic features of complete prokaryotic genomes (CPGs) are crucial for various fields of microbial research and education, yet they are often overlooked. Additionally, creating systematically curated datasets that align with research concerns is an essential yet challenging task for wet-lab researchers. In this study, we introduce CPGminer,more » a user-friendly tool that allows researchers to quickly and easily examine the genomic features and taxonomy of CPGs and curate genome datasets. We also provide several examples to demonstrate its practical utility in addressing descriptive questions.« less

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Coremore » (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.« less

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently beingmore » undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community« less

Digitalisation is one of the key drivers for reducing the costs and risks of wind energy. When considering whether to embark on a digitalisation initiative, two key questions arise. The first is what business or operational opportunities might feasibly be addressed and the second is which of the many potential aspects of digitalisation are relevant to those opportunities. In this work, we show how these questions can be answered with a use-case-driven approach, based around a survey aiming to collect and collate the main "pain points" (or everyday challenges) of people in the wind energy sector. Although the relatively lowmore » number of participants of the survey (46) means that the results should only be used indicatively, it is still possible to make some general recommendations for priorities for digitalisation efforts in the wind energy sector. Firstly, digitalisation efforts should focus both on supporting people carrying out cross-lifecycle tasks, in particular sharing data, managing data, undertaking general data analyses and accessing data. Tools to do this should deal with varying data formats and naming conventions, make metadata more accessible, define data and metadata standards, make more data publicly available and improve the quality of data. Secondly, efforts should also focus on supporting people in the wind farm operational phase, in particular with failure detection, fault diagnosis, failure rate modelling and predictive maintenance. Solutions to do this should focus on accessible and validated tools for fault detection, cloud or other data pipeline solutions for SCADA data and tools for exhaustive data documentation. Finally, digitalisation efforts should focus on better communicating and helping people become aware of existing solutions and tools, as well as on helping people to exert a stronger influence on possible solutions.« less

Despite an ever-growing number of data sets that catalog and characterize interactions between microbes in different environments and conditions, many of these data are neither easily accessible nor intercompatible. These limitations present a major challenge to microbiome research by hindering the streamlined drawing of inferences across studies.

This article attempts to summarize current best practices that support the movement towards enabling researchers to cite and receive credit for their data. The authors are a small representation of the people and organizations trying to make this happen, and we acknowledge that it is not possible to capture all efforts behind this endeavor in 10 Simple Rules. We encourage interested readers to dive deeper by providing related resources along the way.

In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in an analysis, but designing analysis metadata systems has historically received little direct attention. Among other considerations, an ideal metadata tool should be easy to use by new analysers, should scale to large data volumes and diverse processing paradigms, and should enable future analysis reinterpretation. This document, which is the product of community discussions organised by the HEP Software Foundation, categorises types of metadata by scope and format andmore » gives examples of current metadata solutions. Important design considerations for metadata systems, including sociological factors, analysis preservation efforts, and technical factors, are discussed. A list of best practices and technical requirements for future analysis metadata systems is presented. These best practices could guide the development of a future cross-experimental effort for analysis metadata tools.« less