UKSIM 2013 Tsoulkas Keynote

1. ΚΕΝΤΡΟ ΜΕΛΕΤΩΝ ΑΣΦΑΛΕΙΑΣ
CENTER FOR SECURITY STUDIES
UKSIM-AMSS: 15th International Conference on Modelling and
Simulation
Cambridge University (Emmanuel College), 10-12 April, UK.
Semantic Modeling & Monitoring for Real Time
Decision Making: Results and Next Steps
within the Greek Cyber Crime Centre of
Excellence (GCC)

2. Team
Presenter: Vasilis Tsoulkas, PhD, Systems Analysis.
Research Group:
 Dimitris Kostopoulos, MSc, Software Analyst
 George Leventakis, PhD, Security Risk Analyst
 Prokopis Drogkaris, PhD, Security Policy Analyst
 Vicky Politopoulou, MSc, Forensics Analyst
 Parts of this research implemented in cooperation with :
the IT Innovation Center, Southampton , UK.
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3. Presentation Sections
● Motivation & Objectives – FP 7 funded - SERSCIS Proof
of Concept Architecture - successfully delivered to
the EC
● Semantic System Modeling Aspects
(The Dynamic Multi-Stakeholder Approach)
● Semantic Monitoring and Stream Reasoning Process
● Decision Support Tool Interfaces & Risk Analytics
● Next Steps within the Greek Cyber Crime Center of
Excellence (GCC).
3

4. Motivation and Objectives
● The FP7 Project addressed the problem of Dynamic
Modeling and Dynamic Risk Management in Critical
Infrastructures (Cis).
● Today’s CIs are characterized by: Increased
Connectivity (between different Stakeholders) of
their Information and Data Processing Infrastructures
● ….for Increased Performance and Efficiency
● …but this situation introduces new challenges of
Cyber-Physical Threats and their Counter-Measures
4

5. Motivation and Objectives
3 main causes for increased CI vulnerabilities
1.Cyber-Attacks against interconnected Information &
Communication channels can disrupt Exchanged Data
flow and Integrity
2.Local Disruption in one Organization (stake holder)
generates problems elsewhere due to coupling and
dependencies (of data and networks).
3.Reduced Resilience against any cyber-disruptions due
to reduced excess capacity arising from the exchanged
data for increased efficiency requirements
5

6. Objectives
● Implementation of Agile Service Oriented Technologies to
● compose ICT connections related to the critical
infrastructure
● monitor and manage ICT components against well-defined
dependability criteria
● adapt ICT connections in response to disruption or threats
● validation of this approach in Proof of Concept Scenarios
from the air traffic sector (Airport-Collaborative Decision
Making (A-CDM) /EUROCONTROL)
6

7. EUROCONTROL / Airport-Collaborative Decision
Making – European Air Traffic Management
System
Data
Exchange
Service accessible by a consumer (aircraft operator) through SLA
template consumer. The GH is responsible for coordination of
Ramp Services (catering, fuelling, cleaning, baggage handling)
GH: Is an Orchestrator of Ramp Services to have an aircraft ready
for its next flight
7

8. Some Air-Traffic Critical
Parameters
8

9. Data quality and KPIs in A-
CDM
● Data: Confidentiality, Integrity, Alarms, Data Display
● KPIs: Reflect the Quality of Service Delivery
● KPIs data properties: Is the Quality of Time Estimates
Accuracy
Predictability
Stability
● An SLA Architecture was developed with the following
KPIs & Parameters in the Airport Collaborative Decision
Making (A-CDM) context:
• System Availability
• Data Quality
• Data timeliness, delivery deadlines
• Confidentiality
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10. Performance of
Service Providers & KPIs
● The GH performance is characterized by two KPIs:
TOBT (Target Off Block Time) i) accuracy and ii) stability;
i) TOBT accuracy parameter: It is derived by comparison
with a Ref. Value : Actual Ready Time (ARDT).
The Mean SQ. Deviation between TOBT & ARDT is computed for all
flights departing in a day.
ii) TOBT stability parameter : Measures how stable is the
predictor mechanism of the GH (estimates).
• The two KPIs affect the number of take-offs outside a Slot
Tolerance Window (STW).
• Another KPI for overall CDM performance evaluation: Average
Number of Slots / flight. (Ideally: One Slot/Flight)
10

11. Addressing the Modeling Challenge
for Machine Reasoning
For the Dynamic Multi-Stakeholder system we constructed :
4-levels of abstraction
1. A core (ontology) structure: to model the System and its assets
subject to threats and protected by controls
2. A dependability model: describing system independent:
assets, threats, controls using OWL classes and relationships. Security
expertise is encoded in this model.
3. An abstract system model: describes system-specific threats and
controls. Extends the dependability model classes with security
knowledge.
4. A concrete system model: provides a snapshot of the running
system and instances of the participating assets + contextualised
threats & controls.
Each level inherits from its predecessor (parent – child relation).
The final concrete model has simple structure and integrates
knowledge from: Abstract system model and Dependability model.
11

12. Brief Analysis of Ontology &
Models
1. The Semantic Ontology is constructed such that:
● Only OWL Classes are used for design-time modelling
● OWL Instances are used for modelling the Run – Time System
Composition
● Security expertise is added at design time in the OWL classes
2. The Dependability model provides the first step to develop the
Abstract System Model which is a Design – Time Model of the
system that will be composed dynamically “On the Fly” (run-
time).
3. The Concrete Model Generator is connected to the monitoring
subsystem to create a model of the Running System (Current
System Composition).
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13. Main Innovation of the
Approach
● The Modelling approach is constructed using
Semantics Modelling for Machine Reasoning
automated threat analysis and risk estimation when
the system is composed at Run-Time.
● The design – time Service Oriented Dynamic models
are abstract: They describe the structure but NOT the
composition of the system which is NOT KNOWN until
“Run-Time”.
13

14. Core System Domain Ontology
● This basic system structure, determines what reasoning is used
Threat class Description Controls needed
Unauthorized The service processes an unauthorised request Client AuthN + Client
access from an attacker. AuthZ
Unaccountable Type of unauthorized access, designed to get Client AuthN + Client
access the service without paying for it. AuthZ
01/02/2013 14

15. Dependability System Model
(High Level View)
Threat impact is
Threat activity may affect described in terms of
the behaviour of the the intended (or
asset, or controls on the unintended)
asset may reduce the compromise
threat
threatens Threats are sub-
classed to create
Assets are sub-classed 1 generic and system-
to create generic and specific threat types
system-specific asset affects
types Asset Threat
*
1 * blocks/
protects mitigates Control rules define
depends
which controls block
on
or mitigate the
Control compromise of the
threatened asset
Control presence is Controls are sub-
determined by asset classed to create
behaviour generic control types
only
15

16. Dependability System Model
(Controls & Threats)
Dependability
Model Logical
Asset Types &
Relationships
A generic model of dynamic, multi-stakeholder service-oriented systems
including generic threats and threat classification (control) rules
16

17. Threat Types & Threat Scenario
1. Unauthorized Access (to the service)
2. Data traffic Snooping
3. Man in the Middle
4. Client Impersonation
5. Resource Failure
Unauthorized Data Update at Fuelling Service
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18. Dependability Model Controls (sample )
● Controls provide defences against generic Threats
Two broad categories :
● proactive controls block a threat, against the protected asset ;
● reactive controls allow the effect of a threat on the asset to be
mitigated once the threat is carried out .
● Mitigation and Blocking rules are of the form :
ThreatClass(?t) ∧ AssetClass(?a) ∧ threatens(?t,?a) ∧
ControlClass1(?c1) ∧ protects(?c1, ?a) ∧ … ∧ ControlClassN(?cN) ∧
protects(?cN, ?a) → MitigatedTreat(?t)
ThreatClass(?t) ∧ AssetClass(?a) ∧ threatens(?t,?a) ∧
ControlClass1(?c1) ∧ protects(?c1, ?a) ∧ … ∧ ControlClassN(?cN) ∧
protects(?cN, ?a) → BlockedTreat(?t)
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19. Threat Block –
Mitigation Rule Explanation
ThreatClass(?t) ∧ AssetClass(?a) ∧ threatens(?t,?a) ∧
ControlClass1(?c1) ∧ protects(?c1, ?a) ∧ … ∧ ControlClassN(?cN) ∧
protects(?cN, ?a) → MitigatedTreat(?t)
or
ThreatClass(?t) ∧ AssetClass(?a) ∧ threatens(?t,?a) ∧
ControlClass1(?c1) ∧ protects(?c1, ?a) ∧ … ∧ ControlClassN(?cN) ∧
protects(?cN, ?a) → BlockedTreat(?t)
● First line in each case finds a threat instance of a specific type
that threatens an asset instance of a specific type.
● Second line looks for control instances of the right types to
protect the threatened asset against this type of threat.
● Last line classifies the threat as either mitigated or blocked,
depending on the types of controls affording this protection
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20. Control Class Explanation
Control classes provide:
● generic control types that can be included directly
in an abstract system model;
● descriptions of deployment actions: how to deploy
the control into the real system;
● descriptions of mitigation actions: how to operate
reactive controls to protect assets when a threat is
carried out against them.
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21. Resource Software Malfunction
(Mild Error)
● Threat
Provider-
Specified ● a bug in PSResource software
Resource
causes it to repeatedly
produce faults
threatens
protects ● Controls
● PSResource has Suplier
blocks Software Patching: the
Supplier
Resource
Software Software Supplier has a procedure to
Malfunction Patching maintain the software used by
the PSResource
● ensures bug fixes are applied
promptly
● System specifics
● one subclass per PSResource
class
● one instance per PSResource
of each of the resulting classes

22. Abstract System Model of A-
CDM
● It is a design-time model of the structure of the dynamic,
multi-stakeholder service-oriented system: Input for fully
automated run-time model generation and analysis
Tools. It is composed dynamically at Run-Time.
22

23. Concrete System Model in the
Overall
Architecture
● A run-time model of the system, including its current
composition, status and threat-related behaviours
(Current System Composition)
● It is Created automatically, and it is used to provide run-
time decision support
23

24. Classification / Estimation Blocks -
Output
Concrete Model : Input for a) Threat Classification & b) Threat Likelihood Estimation
The output of these two tools provides:
• A list of potential threats with classification
• Estimates of the likelihood a threat is active or in-active
• A description of each Threat with impact severity
• A list of controls to block or mitigate a threat and if these controls
are available in the system
24

25. Semantic Monitoring & Stream Reasoning
Process (Initial Prototype & Limitations)
This initial monitoring – reasoning process has 3-stages:
Starting point: Semantic model of the structure of the dynamical multi –
stakeholder system.
The model was “Abstract” based only on OWL Classes with “types of entities” but
NOT YET “Entity Instances”. These are only known at Run-Time. !!!
At Run-Time the Concrete System Model was Generated with OWL Instances
reflecting the System Composition.
Concrete Model generation used Status Monitoring Data from Run Time
Monitoring and Management Block 25

26. Some…..Limitations of this
Approach
● Run – Time Concrete model reflects instantaneous
snap-shot of System Composition, Behavior & Status.
No past Information. !!
● Threat Analysis depends on Current Concrete Model
and NOT on past Snap-Shots (History). !!
● Threat activity is computed from current concrete
system model NOT on past Snap-Shots (History). !!
● No Correlation between Activity and different threats
26

27. Some…… PoC Implementation
Limitations
● A complete set of monitoring data is needed to
generate the Concrete System Model Snap-shot with
Re-Generation of even static features of the model
each time. !!! The A-CDM PoC.
● This generation of the Complete - Concrete Model is
“Time Consuming”. Every new model is “out-dated” !!
missing intermediate “rapid changes information”.
• Non-Distinguishability between asset – compromises
(Behaviors) that produce the same instantaneous
monitoring data even for different Past Monitoring
data.
27

28. New Approach: Stream Reasoning
●
• Information arrives as a stream of “time-stamped” data
• The Knowledge base can be continuously updated and
reasoning goals are continuously re-evaluated as new assertions
arrive
• Reasoning is implemented from a Finite – Time Window and not
at a Single Instant !!.
• Research Efforts on Stream Reasoning is still at its First Steps and at
its Infancy. 28

29. 3 basic – steps in Stream Reasoning
● Select: Relevant Data from Input Streams by using
Sampling Policies that probabilistically drop stream
elements to address bursty streams of data
(unpredictable peaks).
● Abstract: Sampled streams are input to Abstract block
to generate aggregate events by enforcing aggregate
events continuously.
Output is RDF streams (ρ, τ) with ρ – RDF triple and τ – time
stamp (logical arrival time of RDF statement. Use of C-SPARQL.
● Reason: RDF (Graph Streams) streams are injected into
background knowledge for reasoning tasks.
Incremental implementation of RDF snapshots.
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30. Semantic Monitoring Block
Semantic Reasoning Block ….excluded
30

31. Semantic Monitoring Component
: DSMS - Behavior Analyser - Sequential
Detection
DSMS: Data Stream Management System : samples & filters monitoring data
generated by Service Monitoring and Management Components.
● Usage of open-source CEP (Java - ESPER): Real Time engine that triggers
Listeners or Subscribers using a tailored Event Processing Language (EPL).
31

32. Behavior Analyser (BA)
● Processing of multiple data streams from DSMS.
Produced Output is Graph Triples (RDF).
• Decides how to convert raw monitoring data into
Semantic Assertions related to: Presence of Assets and
Behaviors.
● The monitoring framework generates 2 – types of Time
stamped RDF assertions:
(1) Presence or Absence of Assets (joining or
leaving the system)
(2) Assertions about Measurability, Presence or
Absence of Adverse Behavior of these Assets.
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33. Behavior Analyser (BA)
● The BA is not only a Transcoder converting Monitoring
Events to RDF graphs.
● The BA decides about the type of Behaviors (Assets
and Services).
● Example: The BA is capable to determine if an Asset
is Overloaded or Underperforming using
Monitoring Data for Load and Performance (KPIs – SLA
events).
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34. Sequential (Behavior) Detection
 Well – Known Cumulative Sum (CUSUM) algorithm
from the sequential statistics literature.
 In general parametric models are used
 Change in the mean of the relevant stochastic
process
 We use: The non-parametric version of CUSUM
34

35. Sequential (Behavior) Detection
Mean
Value
Attack Initiation Time and Detection Delay
Test
Statistic
35

36. Non-parametric CUSUM:
Mathematical principles
Random Sequence
yn =( yn −1 +Z n ) + Test Statistic
y0 =0
X + = max(0, x )
If We Define:
The max Continuous Increment up to time n.
0 : Normal Operation
Decision Making Rule:
1 : Attack Event: N is Threshold
36

37. DST – Tool Dynamic Interfaces
Scenario: Remote exploitation on Fuelling Services
GH
Fuelling
Service PSR_InterruptedCommunication Host
Group X
scheduling
Airport NET
Remote exploit
Attacker
AttacK: Attacker on the AirportNet network
targets the Host of the Fuelling Service.
RKE: Remote Known Exploit
37

38. SERSCIS DST interface
(Logical Assets View)
38

39. DST interface (Physical Assets
View)
39

40. DST interface - Assets Under Threat
40

41. DST interface
(Threats Involving Selected Asset)
41

42. DST interface (Threat Information and
Countermeasures Proposition - Zoom)
42

43. EU funded Project
GCC: A Cyber Crime Center of Excellence for Training,
Research and Education in Greece
43

44. Objectives
● To create a Greek Cyber Crime Centre of excellence
& develop a sustainable infrastructure for GCC.
● To mobilize the Greek constituency in the area of
cyber crime training, research, and education.
● To advance research in the area of cybercrime,
focusing particularly in areas dealing with
● cyber attacks,
● botnet research
● Intrusion detection systems
● Intrusion detection systems for Critical Infrastructures.
44

45. Work Packages
GCC Project (36 months)
Starting 1st January 2013
WP1: Management
WP2: Dissemination
WP3: Education and Training
WP4: Research
45

46. Work Packages
GCC Project
FORTH AUTH SAFENET KEMEA
(WP3)
(WP1) (WP2) Training (LEAs,
Management Dissemination
Website(pub./priv.) (WP3) Private/Public
Advisory Board
University 2CENTRE - CCUs sector)
(WP3) courses
Repository (WP4)
(University, LEAs) Research
(WP4)
(WP4) Research (Intrusion
(WP4)
Research Legal issues (legal Detection System
(Disruptive framework, –Critical
monitoring, Botnet Policy) infrastructure
Detection tool) taxonomies)
46

47. GCC
● A CyberCrime Center of Excellence for Training,
Research and Education in Greece” .
● Funded under Prevention of and Fight against Crime
(ISEC) Programme of European Commission
Directorate-General for Home Affairs.
● GCC main objectives are to create a constituency of
Greek stakeholders in the area of Cyber Crime and to
mobilize this constituency to work together in
education and research areas.
47

48. Next Steps in the GCC framework
● Exploitation of sequential detection of a change using the
nonparametric CUSUM in the Behavioral Analyzer.
● Situational Awareness of the Operators using user friendly
Dynamic Support Tool (DST) interfaces
● Further development using additional detection
approaches (Sequential Probability Ratio Test, Different
Optimality Criteria such as: Lorden, Shiryaev - Roberts)
● Distributed Real Time Sequential Detection & Hypothesis
Testing for Intrusion Attacks
● Some Application areas: Industrial CIs Protection, Network
Intrusion Detection Systems, Swarms & Networks of
cooperative UAVs.
48

49. Conclusions
● Implementation of an Intelligent Prototype Tool for the
Protection of Dynamic Multi Stakeholder SOA Critical
Infrastructures. Air-traffic Management Systems PoC.
● Implemented: An Innovative core ontology model
which has been reinforced with rules and classes that
improve threat estimation and classification.
● Implemented: Advanced Stream (RDF) Reasoning – and
Behavioral Analysis Algorithms.
● Sequential data analysis led us to Advanced Semantic
Stream Reasoning for Real –Time Processing.
● Implemented: Dynamic User Interfaces with Risk – Threat
Analytics in Real Time for A-CDM (Eurocontrol).
49

50. Questions – Discussion.
Thank you !
Contact Details:
Vasilis Tsoulkas tsoulkas.kemea@gmail.com
Dimitris Kostopoulos dimkostopoulos@gmail.com
George Leventakis george.leventakis@gmail.com
Prokopis Drogkaris prokopis.drogkaris@gmail.com
Viky Politopoulou v.politopoulou@gmail.com
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