Difference between revisions of "IoTSec:T2.2"
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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. | 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 Implement event driven adaptive security= | + | =[[has subtask::T2.2.1 Implement event driven adaptive security]]= |
* review and extend established models | * review and extend established models | ||
* implement event-driven model addressing events such as RFID-based physical access control or sensor-based voltage monitoring by adapting and extending the work in the MASSIF Project (http://www.massif-project.eu/) for utilising and modelling “events” | * implement event-driven model addressing events such as RFID-based physical access control or sensor-based voltage monitoring by adapting and extending the work in the MASSIF Project (http://www.massif-project.eu/) for utilising and modelling “events” | ||
| − | =T2.2.2 Develop and implement anticipatory adaptive security= | + | =[[has subtask::T2.2.2 Develop and implement anticipatory adaptive security]]= |
* enhance the event-driven implementation addressing the protection of IoT-based smart grids against evolutionary threats, unknown threats | * 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 | * 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 | * 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 | ||
| − | =T2.2.3 develop adaptive user interface with contextual intelligence= | + | =[[has subtask::T2.2.3 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 | * 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 | * 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 | ||
| − | =T2.2.4 Optimize adaptive security models= | + | =[[has subtask::T2.2.4 Optimize adaptive security models]]= |
* improve the accuracy of the adaptive mechanisms for different IoTs processing capabilities by applying optimized machine learning approaches | * improve the accuracy of the adaptive mechanisms for different IoTs processing capabilities by applying optimized machine learning approaches | ||
Revision as of 19:44, 10 September 2015
| 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 |
|---|---|
| WP | IoTSec:WP2 |
| Lead partner | NR |
| Leader | |
| Contributors | NR, Ifi, ESmart Systems |
| edit this task |
Contents
- 1 T2.2 Adaptive Security
- 2 Deliverables in T2.2 Adaptive Security
- 3 Comments
- 4 Detailed objectives
- 5 T2.2.1 Implement event driven adaptive security
- 6 T2.2.2 Develop and implement anticipatory adaptive security
- 7 T2.2.3 develop adaptive user interface with contextual intelligence
- 8 T2.2.4 Optimize adaptive security models
- 9 Expected Results
- 10 Comments
- 11 Earlier descriptions
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 |
Comments
- harmonise T2.2.1... (twice mentioned), I would not see the PhD education as an own sub-task
- be more specific on achieved results with timeline (M12, M24, M36....) - only mention results including M24
- one deliverable looks too little (M12 only), I would expect another deliverable at M24
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 Implement event driven adaptive security
- review and extend established models
- implement event-driven model addressing events such as RFID-based physical access control or sensor-based voltage monitoring by adapting and extending the work in the MASSIF Project (http://www.massif-project.eu/) for utilising and modelling “events”
T2.2.2 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
T2.2.3 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
T2.2.4 Optimize adaptive security models
- improve the accuracy of the adaptive mechanisms for different IoTs processing capabilities by applying optimized machine learning approaches
Expected Results
- One PhD completed (M33) % comment: finish a PhD in earlier than 3 years?
- Working prototype of event-driven 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 - event-driven adaptive security models (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, HIG Waqas Aman(IoTSec PostDoc)
Comments
From here onward can be deleted
Earlier descriptions
T2.2.1 Implementation of adaptive security
Event driven adaptive security addresses events such as RFID-based physical access control or sensor-based voltage monitoring. An event-driven security security architecture is seen as a perfect candidate for real-time security monitoring of the entire grid operations. The basis of the work was addressed in the MASSIF Project (http://www.massif-project.eu/), and will be adapted and extended for utilising and modelling "events".
Adaptive security addresses the protection of "IoT-based smart grids" against evolutionary threats and attacks through the prediction and advanced behavioural analysis of big-real-data from IoT Smart Grids [32]. Our approach on adaptive security is based on [33], and security metrics methodology based on [34]. The adaptive security methodology addresses threats by increasing awareness and automating prevention, detection, and recovery from the failures of security and privacy protections at runtime by re-configuring control parameters and even changing structures and security goals. The objective is, therefore, to develop adaptive security mechanisms using the combination of evolutionary game theory and distributed behavioural analysis for Smart Grids. The expected outcome of this work are modules for use in the operational security.
Result:
- A working prototype implementing adaptive security for IoT in Smart Grids.
The expected outcome of this work are modules for use in the operational security Partners: NR, HIG Waqas Aman(IoTSec PostDoc)
Deliverable:
- 1 technical report,
- 2 conference papers