IoTSec:T2.2
From its-wiki.no
| Security in IoT for Smart Grids | |||||||
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T2.2 Adaptive Security
| Task Title | Adopting and enhancing adaptive security for system of systems |
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| WP | IoTSec:WP2 |
| Lead partner | NR |
| Leader | |
| Contributors | NR, Ifi, ESmart Systems |
| edit this task |
Contents
Objective
This task will review, extend and establish models for
- adaptive security through predication and advanced behavioural analysis of big-real-data
- real-time security monitoring of the entire grid operations
- prevention, detection and recovery from the failures of security and privacy protections
| Category:Task |
Deliverables in T2.2 Adaptive Security
| Title | Due month | Lead partner | Editor | Dissemination level | |
|---|---|---|---|---|---|
| D2.2.1 | Anticipatory adaptive security models (draft) | M12 | NR | Habtamu Abie | Restricted |
| D2.2.2 | Anticipatory adaptive security | M24 | NR | Habtamu Abie | Public |
| D2.2.3 | Adpative user interface report | M30 | NR | Habtamu Abie | Public |
| D2.2.4 | Optimised adaptive security models | M48 | NR | Habtamu Abie | Public |
Detailed objectives
This task will review, extend and establish models for
- adaptive security
- real-time security monitoring of the entire grid operations
The task will further research and develop adaptive security addressing the protection of "IoT-based smart grids" against evolutionary threats and attacks through the prediction and advanced behavioural analysis of big data from IoT Smart Grids by automating prevention, detection, and recovery from the failures of security and privacy protections at run-time and by re-configuring control parameters and security goals.
T2.2.1 Develop and implement anticipatory adaptive security
- enhance the event-driven implementation addressing the protection of IoT-based smart grids against evolutionary threats, unknown threats
- use a combination of evolutionary game theory and advanced behavioral analysis of big data from IoT Smart Grids
- automate prevention, detection, and recovery activities of adaptation (monitoring, analyzing, planning, and execution) at run-time by re-configuring of control parameters and security goals
T2.2.2 develop adaptive user interface with contextual intelligence
- develop adaptive user interface with the ability to reason and anticipate user's situations, serve their needs, and personalize preferences pro-actively
- use modeling user interface relevant contexts and adapting to these contexts with the assurance of the presence of trust status signals and intervenability for users through usable adaptive interfaces
T2.2.3 Optimize adaptive security models
- improve the accuracy of the adaptive mechanisms for different IoTs processing capabilities by applying optimized machine learning approaches
Expected Results
Note: Deliverables D2.2.3 and D2.2.4 have to bee adjusted with existing ones
- One PhD completed (M33) % comment: finish a PhD in earlier than 3 years?
- Functional architecture of adaptive security models (M12)
- Working prototype of adaptive security models (M24)
- Working prototype of adaptive user interface (M30)
- Optimized adaptive security models (M48)
- D2.2.1 technical report - anticipatory adaptive security (draft) (M12)
- D2.2.2 technical report - anticipatory adaptive security (M24)
- D2.2.3 technical report - adaptive user interface (M30)
- D2.2.4 technical report - optimized adaptive security models (M48)
- 8 conference papers (M6-M48) and 5 journal papers (1 paper per year)
The expected outcome of this work are modules for use in the operational security Partners: NR.
Comments
Earlier comments and earlier inputs are in the Discussion page