metamodel for reputation based agents system – case study for electrical distribution scada design

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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

[email protected]

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



Simulations

Conclusions


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

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Introduction

ArchiMate



Simulations

Conclusions


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

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Introduction

ArchiMate


Policy definition

ArchiMate specialisation for MAS and with the policy concept

Policy function of trust


Simulations

Conclusions


Organizational Policy

Application Policy


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


Application policy

Organisational policy

Policy is a function of the trust

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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

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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

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Introduction

ArchiMate



Simulations

Conclusions


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

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Example of

ArchiMate

Instantiation of all agents

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Policies

Table of contents

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Introduction

ArchiMate



Simulations

Conclusions


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%.

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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

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Introduction

ArchiMate



Simulations

Conclusions


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.


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 ?

metamodel for reputation based agents system – case study for electrical distribution scada design

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