#  Introduction

The purpose of this deliverable D1.4 Data Management Plan_Interim version is
to provide an update in the data management lifecycle for the research data
that have so far been or are still foreseen to be collected, generated or
processed by the SecureIoT project, an early view on which was presented in
[1]. As part of making research data FAIR (findable, accessible, interoperable
and reusable) this version of the DMP includes relevant information that will
enable interested researchers and third parties to discover and reuse data
from the SecureIoT project in an easy and efficient way.

Towards that end, Section 2 provides a summary of the research data
collected/generated in the project. Details follow in a later Section which
includes all datasets, even ones that for certain reasons cannot be made
publicly available as well as datasets that while not collected or generated
in the project, have been imported and used in the project (e.g. for training
of algorithms prior to the availability of relevant data coming from the
project’s trialing activities).

Section 3 is about FAIR data, elaborating on the approaches followed and to be
followed by the project to ensure visibility and reusability of the project’s
generated/collected data. Section 4 discusses the allocation of resources and
means of long term preservation of data, while Section 5 presents how research
data are handled in the context of the project to prevent unauthorized access
to them. It is worth noting that, as described in [2], while the project will
not be collecting any kind of personal data from trialing activities
themselves, such data might be collected as part of T6.5 “Stakeholders’
Feedback and Evaluation” activities in the DoA, when collecting feedback and
their opinions about project generated results. Also, project data collected
during the trialing activities themselves, while not personal in nature, they
still need to be securely stored to prevent tampering that would jeopardize
their quality but also due to being commerciallysensitive.

Section 6 presents updates on ethical aspects while Section 7 presents a
detailed list of datasets where for every dataset we include the “as of now”
view for regarding the points mentioned in the previous Sections. Finally,
Section 8 concludes this document.

#  Data Summary

SecureIoT has been collecting, generating and using data in the context of use
case validation in the three following broad domains:

  * Multi-Vendor Industrie 4 Usage Scenarios 

  

  * Socially Assistive Robots and IoT Applications Usage Scenarios 

  

  * Connected Car and Autonomous Driving Usage Scenarios 

  

These data are intended to validate the capability and performance of
SecureIoT components with functionalities ranging from collection of security
data (WP3), to analysis of security data to identify emerging threats (WP4)
and eventually providing assessment of risks, levels of compliance and
securing software components (WP5); which is the main objective of the
project. For more details regarding the specific scenarios in the context of
which these data have been (or will be still) collected, the interested reader
can refer to [3].

Such data, in addition to being useful for SecureIoT project testing purposes,
have the capability to further promote and foster research and development
activities in the broader community in the areas of security research in IoT
in similar or even different contexts depending on the specific deployment
scenario.

In Section 7 we present further details for all datasets under the umbrella of
the respective use cases in terms of:

  * Name 

  

  * Description 

  

  * (Expected) Dataset size 

  

  * Structure 

  

  * Data Utility (i.e. to whom each dataset might be useful) 

  

Also, as described in [4], data for capturing stakeholders’ feedback are
envisioned. It is worth noting that questionnaires to collect stakeholders’
data are currently under review, therefore expected data coming from
stakeholders’ feedback are not currently reported in Section 7 of this D1.4
deliverable (a provisional view of them can be found in Section 4.2.7 of [4]).
Such data will be collected though in the context of all 3 use cases as part
of the evaluation process.

#  FAIR Data

##  Making data findable, including provisions for metadata

Datasets collected/generated in the project, when they are to be deposited in
open data repositories, they will adopt a file naming scheme that will allow
to easily:

  * link them with the SecureIoT project 

  

  * identify the type of data included and structure 

  

  * identify the version of the dataset 

  

With these in mind the datasets produced by the project will be using the
following file naming scheme:

**SecureIoT_UseCase_NameofDataset_DataStructure_FROM:date_TO:date_Location_version.E
xtension**

Just as an example for the sake of presentation, for the 1  st  version of
data coming from a vehicle without any compromise in the Connected Car use
case, which are representative of the date 15/01/2019 and of location
Cambridge, which are JSON entries in a zip file, the filename would be:

**SecureIoT_ConnectedCar_NormalCarData_JSON_20190115_Cambridge_v1.zip**

It is worth noting that if some of the fields in the file naming convention
are not needed for some datasets, e.g. because location is not of interest,
they can be omitted altogether.

This, together with a representative set of keywords (one of which will be
SecureIoT and all the rest accurately reflecting the content of the datasets)
and other associated metadata, based on the description of the dataset, will
allow for easily finding the dataset.

It is worth noting that for depositing datasets that can be made publicly
available (either immediately after they have been collected or after an
embargo period or based on certain restrictions) the Zenodo (
_www.zenodo.org_ )  repository will be used. Zenodo is a free of charge, open
data repository which can handle any file format up to 50GB. Zenodo allows the
uploader to define and store metadata following Zenodo’s metadata standards
and also generates and registers Digital Object Identifiers (DOIs) through
Datacite (  _https://datacite.org/_ )  which is the leading global non-profit
organization for providing DOIs for research data, making DOIs from SecureIoT
accessible in the long term.

##  Making data openly accessible

Datasets collected/generated in the context of the SecureIoT project by
default will be made publicly available unless terms and conditions apply that
would prohibit this (e.g. IPR, commercial-sensitivity etc.). Data from
questionnaires to stakeholders will not be shared though through Zenodo in any
form but will remain solely and strictly for use within the project. For data
that do not need to remain completely closed, as mentioned above, the Zenodo
repository will be used for depositing them. For datasets where restrictions
apply in terms of accessing them, Zenodo eases this process of requesting
being granted access permission by allowing uploaders of data to present the
terms and conditions for access and be notified when a request for access is
issued.

In Section 7, the consortium ‘s stance with respect to openness of datasets as
in the time of writing this document, is presented; this may be revisited in
due course and -if so- this will be reflected in the final version of the Data
Management Plan deliverable [5]. Section 7 also presents the tools that can be
used to open/read the respective datasets.

##  Making data interoperable

Data will be interoperable by following common vocabularies and ontologies
and/or providing clear description of the data structures if they follow less
common formats. Through the clear description of the data structure other
researchers even not using the same data structure, will be able to transform
data accordingly for use by their own custom software tools.

##  Increase data re-use

In order to permit the reuse of data, the datasets will be accompanied by a
relevant license. SecureIoT considers the family of Creative Commons Licenses
(CCL)  ( _https://creativecommons.org/licenses/_ )  as a very straightforward
way to allow the re-use of data as they ensure that the source and authority
of the data are recognized and commercial interests -if applicable- can also
be protected.

The specific version of the CCL license (or any other license - if different
for some reason) used is dataset dependent and is presented in Section 7
together with the datasets owners.

#  Allocation of Resources

In the context of the project it is the role of task T1.3 “Use Cases
Coordination” with its allocated resources to ensure that datasets, as they
are produced, are checked for quality and -if their nature allows- are shared
with the broader public.

Using Zenodo (no fees) ensures that long term preservation of data can be
achieved with negligible associated costs.

Every dataset owner will be responsible for handling the data management of
respective datasets; from their collection/generation to their eventual upload
in Zenodo, when there are no IPR or other reasons which would prohibit them
from being deposited.

#  Data Security

All gathered research data during the course of the project will be securely
handled to prevent them from loss and unauthorized access. Data need to be
securely stored due to their potential personal nature (data from
questionnaires to stakeholders) but also to prevent tampering that would
jeopardize their quality but also due to being commercially-sensitive (data
coming from trialing activities themselves).

The project is applying the following measures, prescribed by the General Data
Protection Regulation (2016/679) to ensure adequate protection during the
project execution for research data with partners involved in the processing
of data, in charge of applying them:

  * Data storage in safe locations, with access limited to authorized persons and partners of the project 

  

  * Safe data transfer through secure, encryption-protected connections 

  

  * Remote access through secure, encryption-protected connections, granting authorization only to persons and activities relevant to the project and within the time frame of the project 

  

  * Close monitoring of access to SecureIoT platform instantiations used for use case testing activities 

  

  * For personal data (if this turns out to be the case), pseudoanonymization or complete anonymization will be applied to remove the link between the stored data and real person identity. For reporting purposes (e.g. in [6] and [7]) only anonymized and aggregated data will be reported to ensure that data subjects cannot be identified. 

  

Regarding data deposited in Zenodo, data security relies on the widely tested
Zenodo platform.

#  Ethical Aspects

Ethical aspects related to activities of the SecureIoT project are managed
within WP9 “Ethics requirements”. As described in [2] personal data will not
be collected as part of trialing activities themselves but might be collected
through questionnaires to collect stakeholders’ feedback. For this activity,
informed consent forms describing why -if this is the case- personal data are
needed, how and for how long they will be stored etc. will be included in the
questionnaires. The template of this consent form is being under review and
will be annexed in the final version of the Data Management deliverable at
M36.

The project is also collecting personal data through its web portal and will
also be collecting personal data through its market platform (WP7). While
these are not research data, for the sake of completeness we include in this
deliverable, as Annexes, the privacy policy of the SecureIoT web portal and
market platform.

#  List of Datasets

In this Section, we present details for datasets in the project; all
representing version 1. As data collection and use is an ongoing process,
details in the list are subject to change. If need be, follow-up versions will
be created to capture any changes during the further course of the project.
When depositing to Zenodo datasets that are not closed, further details
regarding the structure of datasets (e.g. fields and measurement units) will
be provided to assist interested third parties.

##  Multi-Vendor Industrie 4 Usage Scenarios data

###  Datasets collected/generated in the project  
  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#1

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

2019/06/10 - Injection Molding - low rate - normal

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Normal injection molding data with low datarate (1/0.5s)

This file contains datapoints from simulated injection molding cycles within
the industry 4.0 use case. A cycle, i.e. producing one piece of injection
molded product, takes 60 seconds. During this time, molten plastic is injected
into the mold, resulting in a strong increase of pressure and temperature in
the mold and the mold area. The part then cools off until it is cool enough
for the mold to be released.

The dataset contains 13 parameters.

Parameter Explanation

</td> </tr>  
<tr>  
<td>

</td>  
<td>

Date

</td>  
<td>

The date of the simulation

</td>  
<td>

</td> </tr>  
<tr>  
<td>

Time

</td>  
<td>

The timestamp for the respective value

</td> </tr>  
<tr>  
<td>

Heater

</td>  
<td>

Indicates, if the heater is on or off

</td> </tr>  
<tr>  
<td>

T_hopper

</td>  
<td>

The temperature of the plastic hopper (°C)

</td> </tr> </table>  
<table>  
<tr>  
<th>

</th>  
<th>

</th>  
<th>

T_barrel

</th>  
<th>

The temperature of the plastic barrel (°C)

</th>  
<th>

</th> </tr>  
<tr>  
<th>

T_mold

</th>  
<th>

The temperature on the outside of the mold (°C)

</th> </tr>  
<tr>  
<th>

T_machine

</th>  
<th>

The temperature on the outside of the machine (°C)

</th> </tr>  
<tr>  
<th>

P_barrel

</th>  
<th>

The pressure of the barrel (bar)

</th> </tr>  
<tr>  
<th>

P_mold

</th>  
<th>

The in-mold pressure (bar)

</th> </tr>  
<tr>  
<th>

M_piston

</th>  
<th>

Indicates piston movement

</th> </tr>  
<tr>  
<th>

Valve_Filler

</th>  
<th>

Valve position of the filler.

</th> </tr>  
<tr>  
<th>

Valve_Mold_inlet

</th>  
<th>

Valve postion at the mold inlet.

</th> </tr>  
<tr>  
<th>

Valve_Mold_outlet

</th>  
<th>

Valve position at the modl outlet.

</th> </tr>  
<tr>  
<th>

Note that the parameters Heater, M_piston, Valve_Filler,
Valve_Mold_inlet/outlet may not contain correct values at the moment.

</th> </tr>  
<tr>  
<td>

Size

</td>  
<td>

17.8 MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Variable name, Type ( **N** umeric or **A** SCII), Decimals (number of decimal
places in the case of a numeric variable, Writable (1 if can be written by the
machine, 0 if not)

Date,N,0,0,

Time,A,0,0,

Heater,N,0,0,

T_hopper,N,8,0,

T_barrel,N,8,0,

T_mold,N,9,0,

T_machine,N,8,0,

P_barrel,N,9,0,

P_mold,N,8,0,

M_piston,N,0,0,

Valve_Filler,N,0,0,

Valve_Mold_inlet,N,0,0,

Valve_Mold_outlet,N,0,0

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Closed, until further notice (may contain commercially sensitive information)

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

TBD

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Hendrik Eikerling ( _hendrik.eikerling@iem.fraunhofer.de_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#2

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

2019/06/10 - Injection Molding - high rate - normal

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Normal injection molding data with high datarate (1/0.05s) See #1

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

176 MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

See #1

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Closed, until further notice (may contain commercially sensitive information)

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

TBD

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Hendrik Eikerling ( _hendrik.eikerling@iem.fraunhofer.de_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#3

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

2019/06/10 - Injection Molding - low rate - anomalous

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Anomalous injection molding data with low datarate (1/0.5s).

Not all cycles are anomalous - the chance of an anomalous cycle is 50%.

See #1

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

19.3 MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

See #1

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Closed, until further notice (may contain commercially sensitive information)

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

TBD

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Hendrik Eikerling ( _hendrik.eikerling@iem.fraunhofer.de_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#4

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

2019/06/10 - Injection Molding - high rate - anomalous

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Anomalous injection molding data with high datarate (1/0.05s)

Not all cycles are anomalous - the chance of an anomalous cycle is 50%.

See #1

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

192 MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

See #1

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Closed, until further notice (may contain commercially sensitive information)

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

TBD

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Hendrik Eikerling ( _hendrik.eikerling@iem.fraunhofer.de_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#5

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

2019/06/10 - Injection Molding - high rate - anomalous timestamps

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Timestamps of anomalous cycles – high datarate (1/0.05s).

Correspond to anomalous cycles of dataset #4.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

45.4 MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

See #1

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Closed, until further notice (may contain commercially sensitive information)

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

TBD

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Hendrik Eikerling ( _hendrik.eikerling@iem.fraunhofer.de_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#6

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

2019/06/10 - Injection Molding - low rate - anomalous timestamps

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Timestamps of anomalous cycles – low datarate (1/0.5s).

Correspond to anomalous cycles of dataset #3.

</td> </tr>  
<tr>  
<td>

Use case involved

</td>  
<td>

Industrie 4.0 – Injection Molding

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

4.7 MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

See #1

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Closed, until further notice (may contain commercially sensitive information)

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

TBD

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Hendrik Eikerling ( _hendrik.eikerling@iem.fraunhofer.de_ )

</td> </tr> </table>

###  Datasets imported

So far, no open data sets have been identified as sources of information
relevant to the use case; if this changes in later stages of the project, this
section will be updated.

##  Socially Assistive Robots and IoT Applications Usage Scenarios

###  Datasets collected/generated in the project  
  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#1

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Environmental sensing

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Environmental sensing data coming from environmental sensors

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

414 kB sensing data/day

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

{

"_id" : "2014-09-01T00:00:00.000Z",

"_rev" : "1-b34306f2f0344672d653f5b5c7df711c",

"movement" : false,

"illuminance" : 2.0464407112347436, "temperature" : 19.40782989444393,

"humidity" : 52.07199253060395,

"NG" : 1,

"CO" : 2,

"LPG" : 1,

"door_open" : true,

"timestamp" : "2014-09-01T00:00:00.000Z"

}

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Sofoklis Kyriazakos ( _skyriazakos@innovationsprint.eu_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#2

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Wearable sensing

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Wearable sensing data coming from wearable devices

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

3.03 MB wearable sensing data/day

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

{

"_id": "2014-09-01T12:29:10.000Z",

"_rev": "1-156f011ef2ecd6643a089eb61bc0b24e",

"activity": {

"IMA": 0.1112141600593139,

"ISA": 0.1112141600593139,

"steps": 5473,

"physicalActivity": "WALKING"

},

"fall": false,

"timestamp": "2014-09-01T12:29:10.000Z"

}

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Sofoklis Kyriazakos ( _skyriazakos@innovationsprint.eu_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#3

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Visual sensing

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Visual sensing data coming from visual sensors

</td> </tr> </table>  
<table>  
<tr>  
<th>

Size

</th>  
<th>

23 kB visual sensing data/day

</th> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

{

"_id": "2014-09-01T12:29:10.000Z",

"_rev": "1-156f011ef2ecd6643a089eb61bc0b24e",

"people": [

{

"trackID": 0,

"x": 400,

"y": 340,

"width": 40,

"height": 40,

"positionConf": 0.9,

"gender": "MALE", "genderConf": 0.9,

"age": 70,

"ageConf": 0.8,

"emotion": "NEUTRAL",

"emotionConf": 0.7

},

{

"trackID": 2,

"x": 230,

"y": 310,

"width": 43,

"height": 42,

"positionConf": 0.9,

"gender": "MALE", "genderConf": 0.9,

"age": 68,

"ageConf": 0.7,

"emotion": "NEUTRAL",

"emotionConf": 0.7

}

],

"timestamp": "2014-09-01T12:29:10.000Z"

}

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Sofoklis Kyriazakos ( _skyriazakos@innovationsprint.eu_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#4

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Resting furniture sensing

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Resting furniture data coming from furniture sensors

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

1.17 MB resting furniture data/day

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

{

"_id": "2014-09-01T00:00:10.000Z",

"_rev": "1-80ede81818d8a45211212921ae6749a7",

"pressure": true,

"IMA": 0.08626862285226562,

"timestamp": "2014-09-01T00:00:10.000Z"

}

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Sofoklis Kyriazakos ( _skyriazakos@innovationsprint.eu_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#5

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Vitals sensing

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

Vitals sensing data coming from vitals sensors

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

0.33 kB vitals sensing data/day

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

{

"_id": "2015-04-05T10:03:00.000Z",

"_rev": "1-88c1d3e9d1ce463320a70a9c740b5b57",

"SPO2": 99,

"HR": 75,

"HRV": 43,

"systolicBP": 139,

"diastolicBP": 87,

"meanABP" : 92,

"noninvBPPR" : 67

"timestamp": "2015-04-05T10:03:00.000Z"

}

Note: not all devices populate all metadata. Each of the devices may write a
subset of these elements in its JSON file.

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Sofoklis Kyriazakos ( _skyriazakos@innovationsprint.eu_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#6

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Patterns of ‘Challenge’ gesture and corresponding motor _positions_

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

The robot’s gesture controller parses a recorded gesture file and generate
proper motor command. Regardless of the application

</td> </tr>  
<tr>  
<td>

</td>  
<td>

context, the generated motor _positions_ commands were recorded for both
normal and abnormal cases.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

2MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Every dataset contains

  * motors position CSV which includes timestamp and motor joint 

  

positions

  * another CSV file which indicates at which time stamp the normal and abnormal cases are generated 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

_License_

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Pouyan Ziafati ( _ziafati@luxai.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#7

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Patterns of ‘Show_right” gesture and corresponding motor _positions_

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

The robot’s gesture controller parses a recorded gesture file and generate
proper motor command. Regardless of the application context, the generated
motor _positions_ commands were recorded for both normal and abnormal cases.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

1MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Every dataset contains

  * motors position CSV which includes timestamp and motor joint 

  

positions

  * another CSV file which indicates at which time stamp the normal and abnormal cases are generated 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Pouyan Ziafati ( _ziafati@luxai.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#8

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Patterns of ‘Challenge’ gesture and corresponding motor _velocities_ _(FAST)_

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

The robot’s gesture controller parses a recorded gesture file and generate
proper motor command. Regardless of the application context, the generated
motor _velocities_ commands were recorded for both normal and abnormal cases.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

1MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Every dataset contains

  * motors velocities CSV which includes timestamp and motor joint 

  

velocities

  * another CSV file which indicates at which time stamp the normal and abnormal cases are generated 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Pouyan Ziafati ( _ziafati@luxai.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#9

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Patterns of ‘Challenge’ gesture and corresponding motor _velocities_ _(SLOW)_

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

The robot’s gesture controller parses a recorded gesture file and generate
proper motor command. Regardless of the application context, the generated
motor _velocities_ commands were recorded for both normal and abnormal cases.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

1MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Every dataset contains

  * motors velocities CSV which includes timestamp and motor joint 

  

velocities

  * another CSV file which indicates at which time stamp the normal and abnormal cases are generated 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Pouyan Ziafati ( _ziafati@luxai.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#10

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Patterns of ‘Show_right” gesture and corresponding motor _velocities_ _(FAST)_

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

The robot’s gesture controller parses a recorded gesture file and generate
proper motor command. Regardless of the application context, the generated
motor _velocities_ commands were recorded for both normal and abnormal cases.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

1MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Every dataset contains

</td> </tr>  
<tr>  
<td>

</td>  
<td>

  * motors velocities CSV which includes timestamp and motor joint 

  

velocities

  * another CSV file which indicates at which time stamp the normal and abnormal cases are generated 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Pouyan Ziafati ( _ziafati@luxai.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#11

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Patterns of motors data during specific application content

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

For the normal case, the QT MemGame demo is played by a user and the motors
positions are logged during the different runs of the game together with the
start/end time of each run.

For the abnormal case QT MemGame demo is played by a user but the behavior of
the game disturbed by some irrelevant gestures and moving motors to some
positions which should not happen within this application content.

The motors positions, start/end time of each run of the game and the attack
(abnormal cases) times are logged.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

1MB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

Every dataset contains

  * motors positions CSV which includes timestamp and motor joint positions 

  

  * another CSV file which indicate at which time stamp the normal and abnormal cases are generated 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

Researchers

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and Microsoft Excel or another tool to open CSV files

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

Pouyan Ziafati ( _ziafati@luxai.com_ )

</td> </tr> </table>

###  Datasets imported

So far, no open data sets have been identified as sources of information
relevant to the use case; if this changes in later stages of the project, this
section will be updated.

##  Connected Car and Autonomous Driving Usage Scenarios

###  Datasets collected/generated in the project  
  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#1

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Bilbao. Normal datasets.

Generated: 2019/07/02

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Bilbao.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

There is no (intended) weird behaviour or attack included.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Three trips are included:

  * Bilbao0 

    * DriverRecord JSON file: 434KB o CANBeat: 92KB 

  

  

  * Bilbao1 

    * DriverRecord JSON file: 445KB o CANBeat: 94KB 

  

  

</td> </tr>  
<tr>  
<td>

</td>  
<td>

• Bilbao2

o DriverRecord JSON file: 340KB o CANBeat: 72KB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data 

  

{"bra": "0.0", "dist": "0.00", "element": "1", "fue": "0.00", "gear":

"2", "ignition": "0", "lat": "52.23741", "lon": "0.15823", "rpm": "1000",
"speed": "0.00", "str_ang": "-1.5", "throttle": "0.0", "timestamp":
"2019-01-15 14:58:07.469314", "v2xLat":

"52.23741", "v2xLon": "0.15823"}

  * CAN data (CANBeat) 

  

{"cbus_load": "9", "invl_crcs": "0", "invl_seqs": "0", "timestamp":

"2019-05-21 15:03:17.248674", "unex_dlcs": "0", "unex_msgs": "0"}

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#2

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Cologne. Normal datasets with a manipulated (CAN
& Vehicle Data attacks) version of Cologne0 for comparison.

Generated: 2019/07/02

</td> </tr> </table>  
<table>  
<tr>  
<th>

Description

</th>  
<th>

This dataset represents vehicle information collected while driving around
Cologne.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

‘ _Cologne0_ ’and ‘ _Manipulated Cologne0_ ’ are based on the same “trip”, of
which the manipulated version has some unusual behaviour both in the
application level data (DriverRecord) and in the CAN activity (CANBeat).

Other than ‘ _Manipulated Cologne0_ ’, there is no (intended) weird behaviour
or attack included in these sets.

</th> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Three trips are included:

  * Cologne0 o DriverRecord JSON file: 250B o CANBeat JSON file: 53KB 

  

  * Manipulated version of Cologne0 o DriverRecord JSON file: 251KB o CANBeat JSON file: 54KB 

  

  * Cologne1 o DriverRecord JSON file: 350KB o CANBeat JSON file: 75KB 

  

  * Cologne2 o DriverRecord JSON file: 249KB o CANBeat JSON file: 53KB 

  

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#3

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Munich. Normal datasets with a manipulated (CAN &
Vehicle Data attacks) version of Munich0 for comparison.

Generated: 2019/07/02

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Munich.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

‘ _Munich0_ ’and ‘ _Manipulated Munich0_ ’ are based on the same “trip”, of
which the manipulated version has some unusual behaviour both in the
application level data (DriverRecord) and in the CAN activity (CANBeat).

Other than ‘ _Manipulated Munich0_ ’, there is no (intended) weird behaviour
or attack included in these sets.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Two trips are included:

  * Munich0 o DriverRecord JSON file: 254KB o CANBeat JSON file: 54KB 

  

  * Manipulated version of Munich0 o DriverRecord JSON file: 255KB 

  

o CANBeat JSON file: 55KB

  * Munich1 o DriverRecord JSON file: 299KB 

  

</td> </tr>  
<tr>  
<td>

</td>  
<td>

o CANBeat JSON file: 63KB

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#4

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Paris. Normal datasets with a manipulated (CAN &
Vehicle Data attacks) version of Paris0 for comparison.

Generated: 2019/07/02

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Paris.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

‘ _Paris0_ ’and ‘ _Manipulated Paris0_ ’ are based on the same “trip”, of
which the manipulated version has some unusual behaviour both in the
application level data (DriverRecord) and in the CAN activity (CANBeat).

Other than ‘ _Manipulated Paris0_ ’, there is no (intended) weird behaviour or
attack included in these sets.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Five trips are included:

  * Paris0 o DriverRecord JSON file: 350KB o CANBeat JSON file: 75KB 

  

  * Manipulated version of Paris0 o DriverRecord JSON file: 350KB o CANBeat JSON file: 75KB 

  

  * Paris1 o DriverRecord JSON file: 275KB o CANBeat JSON file: 59KB 

  

  * Paris2 o DriverRecord JSON file: 306KB o CANBeat JSON file: 65KB 

  

  * Paris3 o DriverRecord JSON file: 337KB o CANBeat JSON file: 72KB 

  

  * Paris4 o DriverRecord JSON file: 222KB o CANBeat JSON file: 48KB 

  

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#5

</th> </tr> </table>  
<table>  
<tr>  
<th>

Name

</th>  
<th>

Mature development datasets, Athens. Normal datasets with a manipulated (CAN &
Vehicle Data attacks) version of Athens0 for comparison.

Generated: 2019/07/02

</th> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Athens.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

‘ _Athens0_ ’and ‘ _Manipulated Athens0_ ’ are based on the same “trip”, of
which the manipulated version has some unusual behaviour both in the
application level data (DriverRecord) and in the CAN activity (CANBeat)

Other than ‘ _Manipulated Athens0_ ’, there is no (intended) weird behaviour
or attack included in these sets.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Three trips are included:

  * Athens0 o DriverRecord JSON file: 278KB o CANBeat JSON file: 59KB 

  

  * Manipulated version of Athens0 o DriverRecord JSON file: 279KB o CANBeat JSON file: 60KB 

  

  * Athens1 o DriverRecord JSON file: 276KB o CANBeat JSON file: 58KB 

  

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#6

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Brussels. Normal datasets

Generated: 2019/07/02 & 2019/07/03

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Brussels.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

There is no (intended) weird behaviour or attack included in these sets.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Three trips are included:

  * Brussels0 o DriverRecord JSON file: 362KB o CANBeat JSON file: 77KB 

  

  * Brussels1 o DriverRecord JSON file: 266KB o CANBeat JSON file: 57KB 

  

  * Brussels2 o DriverRecord JSON file: 184KB o CANBeat JSON file: 39KB 

  

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

• Cybersecurity experts aiming to understand the normal and abnormal
performance of a connected vehicle.

</td> </tr>  
<tr>  
<td>

</td>  
<td>

• Providers interested in the development of services for connected and
autonomous vehicles.

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#7

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Waterford. Normal datasets with a manipulated
(CAN & Vehicle Data attacks) version of Waterford0 for comparison.

Generated: 2019/07/02 & 2019/07/03

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Waterford.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

‘ _Waterford0_ ’and ‘ _Manipulated Waterford0_ ’ are based on the same “trip”,
of which the manipulated version has some unusual behaviour both in the
application level data (DriverRecord) and in the CAN activity (CANBeat).

Other than ‘ _Manipulated Waterford0_ ’, there is no (intended) weird
behaviour or attack included in these sets.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Three trips are included:

  * Waterford0 o DriverRecord JSON file: 484KB o CANBeat JSON file: 102KB 

  

  * Manipulated version of Waterford0 

  

</td> </tr>  
<tr>  
<td>

</td>  
<td>

o DriverRecord JSON file: 484KB o CANBeat JSON file: 103KB

  * Waterford1 o DriverRecord JSON file: 250KB o CANBeat JSON file: 53KB 

  

  * Waterford1 o DriverRecord JSON file: 431KB o CANBeat JSON file: 91KB 

  

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>  
<table>  
<tr>  
<th>

Number

</th>  
<th>

#8

</th> </tr>  
<tr>  
<td>

Name

</td>  
<td>

Mature development datasets, Cambridge. Normal datasets with a manipulated
(CAN & Vehicle Data attacks) version of Cambridge0 for comparison.

Generated: 2019/07/02

</td> </tr>  
<tr>  
<td>

Description

</td>  
<td>

This dataset represents vehicle information collected while driving around
Cambridge.

The dataset contains multiple vehicle signals as collected by the IDAPT
onboard vehicle-unit from the vehicle CAN networks, IMU and V2X. In addition,
information gathered from the vehicle CAN bus by SecureIoT CANBeat is also
included.

</td> </tr>  
<tr>  
<td>

</td>  
<td>

‘ _Cambridge0_ ’and ‘ _Manipulated Cambridge 0_ ’ are based on the same
“trip”, of which the manipulated version has some unusual behaviour both in
the application level data (DriverRecord) and in the CAN activity (CANBeat).

Other than ‘ _Manipulated Cambridge0_ ’, there is no (intended) weird
behaviour or attack included in these sets.

</td> </tr>  
<tr>  
<td>

Size

</td>  
<td>

Three trips are included:

  * Cambridge0 o DriverRecord JSON file: 499KB o CANBeat JSON file: 106KB 

  

  * Manipulated version of Cambridge0 o DriverRecord JSON file: 500KB o CANBeat JSON file: 107KB 

  

  * Cambridge1 o DriverRecord JSON file: 292KB o CANBeat JSON file: 63KB 

  

  * Cambridge2 o DriverRecord JSON file: 581KB o CANBeat JSON file: 124KB 

  

</td> </tr>  
<tr>  
<td>

Structure

</td>  
<td>

  * Vehicle data: see previous datasets 

  

  * CAN data (CANBeat) : see previous datasets 

  

</td> </tr>  
<tr>  
<td>

Utility

</td>  
<td>

  * Cybersecurity experts aiming to understand the normal and abnormal performance of a connected vehicle. 

  

  * Providers interested in the development of services for connected and autonomous vehicles. 

  

</td> </tr>  
<tr>  
<td>

Openness

</td>  
<td>

Open

</td> </tr>  
<tr>  
<td>

Tool needed

</td>  
<td>

Unzipper and text editor

</td> </tr>  
<tr>  
<td>

License

</td>  
<td>

Creative Commons Attribution 4.0 International Public License

</td> </tr>  
<tr>  
<td>

Owner

</td>  
<td>

David Evans ( _David.Evans@idiada.com_ )

</td> </tr> </table>

###  Datasets imported

So far, no open data sets have been identified as sources of information
relevant to the use case; if this changes in later stages of the project, this
section will be updated.

#  Conclusions

This deliverable presented the current view of the SecureIoT project in terms
of datasets that have been or will be collected/generated and used in the
context of the project.

The final version of the DMP at M36 will present a final and definite list of
datasets that will have been collected/generated by the end of the project;
unless a dataset needs to remain closed for reasons clearly explained, all the
other will have been uploaded to the Zenodo repository accompanied by suitable
metadata, keyworks, licenses and descriptions in general that will ease their
reuse by other interested third parties.