Dataset Information

Dataset of shell commands used by participants of hands-on cybersecurity training.

ABSTRACT: We present a dataset of 13446 shell commands from 175 participants who attended cybersecurity training and solved assignments in the Linux terminal. Each acquired data record contains a command with its arguments and metadata, such as a timestamp, working directory, and host identification in the emulated training infrastructure. The commands were captured in Bash, ZSH, and Metasploit shells. The data are stored as JSON records, enabling vast possibilities for their further use in research and development. These include educational data mining, learning analytics, student modeling, and evaluating machine learning models for intrusion detection. The data were collected from 27 cybersecurity training sessions using an open-source logging toolset and two open-source interactive learning environments. Researchers and developers may use the dataset or deploy the learning environments with the logging toolset to generate their own data in the same format. Moreover, we provide a set of common analytical queries to facilitate the exploratory analysis of the dataset.

SUBMITTER: Svabensky V

PROVIDER: S-EPMC8479389 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Dataset of shell commands used by participants of hands-on cybersecurity training.

Švábenský Valdemar V Vykopal Jan J Seda Pavel P Čeleda Pavel P

Data in brief 20210922

We present a dataset of 13446 shell commands from 175 participants who attended cybersecurity training and solved assignments in the Linux terminal. Each acquired data record contains a command with its arguments and metadata, such as a timestamp, working directory, and host identification in the emulated training infrastructure. The commands were captured in Bash, ZSH, and Metasploit shells. The data are stored as JSON records, enabling vast possibilities for their further use in research and d ...[more]

PMID: 34621929

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Project description:BackgroundAmyloids are proteins capable of forming aberrant intramolecular contact sites, characteristic of beta zipper configuration. Amyloids can underlie serious health conditions, e.g. Alzheimer's or Parkinson's diseases. It has been proposed that short segments of amino acids can be responsible for protein amyloidogenicity, but no more than two hundred such hexapeptides have been experimentally found. The authors of the computational tool Pafig published in BMC Bioinformatics a method for extending the amyloid hexapeptide dataset that could be used for training and testing models. They assumed that all hexapeptides belonging to an amyloid protein can be regarded as amylopositive, while those from proteins never reported as amyloid are always amylonegative. Here we show why the above described method of extending datasets is wrong and discuss the reasons why the incorrect data could lead to falsely correct classification.ResultsThe amyloid classification of hexapeptides by Pafig was confronted with the classification results from different state of the art computational methods and the outputs of all methods were studied by clustering analysis. The clustering methods show that Pafig is an outlier with regard to other approaches. Our study of the statistical patterns of its training and testing datasets showed a strong bias towards STVIIE hexapeptide in their positive part. Different statistical patterns of seemingly amylo-positive and -negative hexapeptides allow for a repeatable classification, which is not related to amyloid propensity of the hexapetides.ConclusionsOur study on recognition of amyloid hexapeptides showed that occurrence of incidental patterns in wrongly selected datasets can produce falsely correct results of classification. The assumption that all hexapeptides belonging to amyloid protein can be regarded as amylopositive and those from proteins never reported as amyloid are always amylonegative is not supported by any other computational method. This is in line with experimental observations that amyloid propensity of a full protein can result from only one amyloidogenic fragment in this protein, while the occurrence of amyliodogenic part that is well hidden inside the protein may never lead to fibril formation. This leads to the conclusion that Pafig does not provide correct classification with regard to amyloidogenicity.

Project description:This dataset contains expert assessments of the cybersecurity skills required for six job profiles in Europe, as determined via surveys responded by cybersecurity experts from academia and industry. The data can be used to identify educational needs in the cybersecurity sector and compare against other frameworks. The six cybersecurity-oriented job profiles used in the surveys are: General cybersec auditor; Technical cybersec auditor; Threat modelling engineer; Security engineer; Enterprise cybersecurity practitioner; Cybersecurity analyst. Data-i.e. expert assessments-was collected via surveys, targeted at European experts in cybersecurity from academia and industry. Respondents characterised the skills needed to perform in six job profiles using the CSEC+ framework: a cybersecurity skills framework prepared as a spreadsheet where cybersecurity skills must be ranked in a Likert scale from 0 (irrelevant) to 4 (advance knowledge needed). Metadata requested included the type of organisation of the respondent (Large company, SME, Academic/Research, Public administration, Other) and the country of origin. There were three data-collection phases: (1) an initial phase, used also to refine later larger-scale processes, carried out in Oct 2021-Jan 2022 and resulting in 13 expert assessments from four EU countries; (2) a second phase implemented as an online service broadcast to a larger audience, carried out in Mar-Apr 2022 and resulting in 15 assessments from eight European countries; (3) and a third phase, allowing direct online input and distributed in PC and mobile form, carried out in Sep-Oct 2022 and resulting in 32 assessments from ten European countries. The raw data gathered was stored and processed via spreadsheets, computing statistical information (mean, stdev) on how much each cybersecurity skill and area was deemed necessary to perform in each job profile. This is visualised as a heatmap where colour intensity symbolises value, and circle diffusion symbolises spread. Processed data further includes visualisations on how the area of origin of the respondent (academia, as in "producer of education", vs. industry, as in "consumer of education") influences the responses. This is shown as bar plots, where whiskers represent confidence intervals used for statistical-significance tests. This data can serve as basis to understand the educational needs for the cybersecurity sector in Europe. It can be reused for comparison against frameworks, other than CSEC+, to assess the need of education in specific cybersecurity sectors such as human security. Furthermore, the Qualtrics survey template (included) is a ready-made solution for replication studies.

Dataset Information

Dataset of shell commands used by participants of hands-on cybersecurity training.

Publications

Dataset of shell commands used by participants of hands-on cybersecurity training.

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