IoTSec:Phone 2019/12/04


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Security in IoT for Smart Grids
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Title IoTSec:Phone Meeting December2019
Phone number, info +47 21933751 725-796-213
Date, time, Duration 2019/12/04, 14:00, 60 min
Organizer Christian Johansen
Participants Christian Johansen

this page was created by Special:FormEdit/Phone_IoTSec, and can be edited by Special:FormEdit/Phone_IoTSec/IoTSec:Phone 2019/12/04

Call Info

or phone Norway: +47 21 93 37 51 Access Code: 725-796-213

More phone numbers FI: +358 923 17 0568 DE: +49 692 5736 7317 IT: +39 0 247 92 13 01 NL: +31 202 251 017 ES: +34 932 75 2004 SE: +46 853 527 827

Help on joining can be taken from IoTSec:Meetings#Help_for_using_GoToMeeting_conference_tool


  • Status for the joint IoTSec papers and general project goal fitting
  • Status from each partner (at least from PhD students including update on time-line)
  • AOB


Minutes of the meeting IoTAdmin:20191204PhoneMeeting_Notes

short notes

  • What are the Main Takeaways from IoTSec ?
  • What are the Major achievements?



  • Christian to send e-mail about the Common Paper writing process. And update the wiki Task 0.2. Include Magne from SIN in the mailing list. Think of producing a poster, rollup, or video for IoTSec. Keep monitoring the work on the common paper, and produce a visual presentation that would include all the partner works and contirbutions to the project.
  • New course at UiO on Smart Grid and IoT



  • Paper "A Taxonomy of Situations within the Context of Risk Analysis" presented at conf in Helsinki.
  • Next paper submitted, currently under review
  • Work on remaining pieces of work to meet deadlines set previously
  • Add contribution to joint paper when contribution platform is ready


  • Proposed and drafted an umbrella paper
    • Safe and secure IoT-enabled smart power grid infrastructure: The IoTSec Project
  • Making progress on contributions to 4 sections of this paper
    • 2.3 Attacks Detection
    • 3.1 Privacy-aware Models
    • 3.2 Adaptive Security Models
    • 4.1 Multi-metrics
  • Making progress on the real time adaptive data collection
    • For adapting collection strategies we have tested Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine (SVM) algorithms from scikit-learn package using python 3 and the following datasets: KDDCup-99, CIC IDS 2017, and BoT-IoT dataset.

other discussion