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Presentation of "Metamodel for Reputation Based Agents System – Case Study for Electrical Distribution SCADA Design" at SINCONF 2013 conference, Aksaray, Turkey
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1
Metamodel for Reputation based Agents System Case Study for Electrical Distribution SCADA Design
Guy Guemkam, Jonathan Blangenois, Christophe Feltus, Djamel Khadraoui
Laboratoire dinformatique de Paris 6, France Faculty of Computer Science, University of Namur, Belgium
Public Research Centre Henri Tudor, Luxembourg-Kirchberg, Luxembourg
October 13-16, 2013
Table of contents
2
Introduction
ArchiMate
Policy concept and trust value
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
Table of contents
3
Introduction
ArchiMate
Policy concept and trust value
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
Introduction
- Critical Infrastructures are essential for the functioning of a
society and economy
4 statements:
- CI are monitored and secured by SCADA systems
- SCADA are deployed using agents whish are governed by
policies
- Agents behave based on their own perception of the evolving
environment and according the perceived trust
- SCADA operates at different abstraction levels of the CI
October 2013 SMC IEEE conference 4
Introduction
Additionally:
- No integrated approach for designing, managing and
monitoring SCADA systems policies
- No consideration of the trust and reputation existing amongst
the agents
Our goal:
Agents modelling framework based on ArchiMate
Integration of Trust based policy
October 2013 SMC IEEE conference 5
Table of contents
6
Introduction
ArchiMate
Policy concept and trust value
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
ArchiMate, the theory
- Enterprise architecture metamodel
- 3 abstraction layers (business, application and technical)
- 3 families of concepts: structural, behavioral, informational
- ArchiMate core concepts:
http://pubs.opengroup.org/architecture/archimate2-doc/
7 October 2013 SMC IEEE conference
ArchiMate
metamodel
6/16/2014 Presentation Tudor 8
Table of contents
9
Introduction
ArchiMate
Policy concept and trust value
Policy definition
ArchiMate specialisation for MAS and with the policy concept
Policy function of trust
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
Organizational Policy
Application Policy
10 October 2013 SMC IEEE conference
The set of rules that achieves the organizational strategy
That governs the execution of behaviours which serve the
realization of organizational services That are executed by means of processes, which occurs in a specific
context, symbolized by a configuration of the business object
The set of rules that achieves the application strategy
That governs the execution of behaviours that serve the realization of application services
That are executed by means of applications, which occurs in a
specific context, symbolized by a configuration of data objects
ArchiMate
metamodel
for MAS
Allows defining:
1. Organizational policy
2. Application policy
Policy is defined as a
behavioral rule which is
associated to a concept
from the architecture
11 October 2013 SMC IEEE conference
Application policy
Organisational policy
Policy is a function of the trust
12
The rules defined by the policy is function of the level of trust that each agent puts in another.
To derive the level of trustworthiness the agent exploits
information provided by probes.
The implementation of trust mechanisms are translated into
agent through the concept of Policies called Trust Policies.
Policy and trust value
13
The trust value of a component at an upper level is derived from
sublevels agents.
That signifies that, for two given agents A and B, the trust value of agent
B computed by agents A is calculated using the equation adapted
from Guemkam et al. as such:
TAB=ORAB= DRAB+ (1-)(1IRi1B+ 2IRi2B+1IRi3B)
with 1+2+2=1 and 0
Table of contents
14
Introduction
ArchiMate
Policy concept and trust value
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
Case Study: Electric power distribution
The ACE Agents collects, aggregates and analyses network information and confirms alerts are sent to the PIE
The PIE Agents receives a confirmed alert from the ACE, set the severity level and the extent of the network response (depending on the alert layer). The high
level alert messages are transferred to the RDP.
15 Septembre 2013 FARES workshop
Example of
ArchiMate
Instanciation of the ACE agent
16
Example of
ArchiMate
Instantiation of all agents
17
Policies
Table of contents
18
Introduction
ArchiMate
Policy concept and trust value
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
Simulation / Environment
We have simulated a heterogeneous network of ACE and PIE
agents running the reputation model.
The framework used for the test environment has been developed
in JAVA and simulate MAS network in a graphical environment.
Each created agent is deployed and is only connected to a central
supervisor (Composed of an Agent Manager and a Graph
Supervisor) that gives him the list of his neighbors depending
of his location on the network with a maximum edge size
between agents.
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Simulation Protocole
The protocol used asks ACE agents to send a message containing
the collected data from the probe to the nearest PIE every five
seconds.
Test environment represents a city of 50x50km with a maximum of
5 kilometers connection distance between agents.
Simulations have been running several times during 120 seconds
with different load of malicious agents, respectively 10%, 50%
and 90%.
20
Simulation results
For each load of malicious agents in the network we have collected
the trust table of the same PIE agent, representing his perception
of his neighbors ACE
As the percentage of malicious growth, the threshold evolves
according to the reputation.
Depending on the connection amongst the agent, the reputation
increases, decreases or fluctuates
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Malicious percentage
10% 50% 90%
ACE Rep ACE Rep ACE Rep
A73 0.8 A73 0.75 A73 0.62
A71 0.86 A71 0.87 A71 0.81
A80 0.69 A80 0.55 A80 0.15
A45 0.72 A45 0.98 A45 0.76
A55 0.91 A55 0.93 A55 0.9
A56 0.93 A56 0.0 A56 0.36
A66 0.82 A66 0.85 A66 0.72
A32 0.8 A32 0.81 A32 0.44
A35 0.84 A35 0.92 A35 0.99
A0 0.73 A0 0.71 A0 0.66
Table of contents
22
Introduction
ArchiMate
Policy concept and trust value
Case study presentation
Simulations
Conclusions
October 2013 SMC IEEE conference
Conclusions
We have elaborated a specialisation of ArchiMate for MAS
purpose to enrich the agents society collaborations
An trust based policy has been introduced and described to
enhance the modelling of the agent evolution in its
environment
Finally, we have simulated a heterogeneous network of ACE and
PIE agents running the reputation model with different load of
malicious agents.
As future works, additional validations are expected in the next
months on larger scale infrastructures. In parallel, a supporting
tool is being developed.
23 October 2013 SMC IEEE conference
Acknowledgments
The research described in this paper is funded by the
CockpitCI research project within the 7th framework
Programme (FP7) of the European Union (EU) (topic SEC-
2011.2.5-1 Cyber-attacks against critical infrastructures Capability Project).
Thank you for your attention !
Any questions ?