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A. Mourad 1
Dr. Azzam Mourad
Assistant ProfessorDepartment of Computer Science and MathematicsLebanese American University (LAU)
Security Issues and Directions of Intelligent Transport Systems within limited-resources constraints
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A. Mourad 2
Research Interest
• Information Security• Security Hardening• Web Services Security• MANET/VANET Security• Trust in Web Services• Mobile Cloud
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A. Mourad 3
Outline
• Project Overview• Security of Composite Services
• AspectBPEL• SBA-XACML
• Selfish Node Detection in VANET• Efficient Clustering Model• Cooperative Detection Model
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A. Mourad 4
Intelligent Transport Systems• Contribute in solving several daily life problems
Control real-time traffic Manage incident Reduce the environment pollution Reduce time Delay Reduce Financial Loss Reduce Energy/Gaz Consumption Boost the productivity and expand economic growth
• Lack of ITS infrastructure in developing countrieso Lead to lack of information for intelligent decisionso Need to provide alternative solutions based on
Multiple and diverse source of information Avoiding the costly infrastructure sources
• Interest of advanced country is in reducing the high cost of infrastructure maintenance and upgrades
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A. Mourad 5
Project Overview
• Challenges Services CompositionAdaptability and
CooperationContext-awarenessQoSSecurity, Trust and PrivacyModels and Algorithms for
Traffic Management and Intelligent Decision Modules
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A. Mourad 6
Partners and Collaborators• Lebanon
CNRS Lebanon Lebanese American University (LAU) Lebanese University Private Sectors
• France LIMOS
• Canada Concordia University ETS Montreal
• UAE Khalifa University
• Looking for other international partners
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A. Mourad 7
Outline
• Project Overview• Security of Composite Services
• AspectBPEL• SBA-XACML
• Selfish Node Detection in VANET• Efficient Clustering Model• Cooperative Detection Model
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A. Mourad 8
Introduction
WSs are emerging as convenient mechanism for automated interaction between distributed applications
Motivations
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A. Mourad 9
Introduction
Nevertheless, the successful deployment of this technology cannot hide the security breaches
and threats that Web services can be exposed to.
Web Service
Motivations
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A. Mourad 10
Introduction
Web Service
Motivations
SAML
…
WS-Security
SAML , WS-Security and other standard security languages emerged to offer message-
level security for web services.
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A. Mourad 11
Introduction
Web Service
Web Service
Web Service
Motivations
SAML
…
WS-Security
However, the problem arises when several distributed and/or independent Web services are
composed together in a process to form a complex system.
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A. Mourad 12
Web Service
Web Service
1- SOAP Request: GetActivity/Weather Parameter : 12345
2- SOAP Request: GetWeatherInfo, Parameter : 12345
3- SOAP Response : Rainy
6- SOAP Response: Shopping
4- SOAP Request: Rainy
5- SOAP Response: Shopping
BPEL Example: Weather Forecast Process
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A. Mourad 13
UDDI1- Where can I find a weather forecast service?2- There is a “Weather Service” in Server B
Web Service
3- How exactly should I invoke you?
4- Take a look at this WSDL
5- WSS SOAP Request
6- SOAP Response: Rainy
<soap:Envelope <soap:Header> <wsse:Security> <xenc:EncryptionMethod Algorithm = "http://www.w3.org/2001/04/xmlenc#tripledes-cbc" /> </wsse:Security> </soap:Header> <soap:Body> <xenc:CipherData> <xenc:CipherValue > InmSSXQcBV5UiT </xenc:CipherValue> </xenc:CipherData> </soap:Body></soap:Envelope>
BPEL Example: WS-Security
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A. Mourad 14
Problem 1
SAML WSS
…
SAML WSS
…
SAML WSS
…
BPEL is only given theresponsibility of business
modeling.Message-level security
at each individual web service.
Performance Issue !Need for
centralization !
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A. Mourad 15
Problem 1Possible solution may be to harden the security of a BPEL processto embed the security verification code within the business logic of such process.
With the use of the current BPEL: • There is a lack of modularity for modeling cross-cutting concerns :
Security, Logging, monitoring, etc…
• No support at the process deployment level for changing the composition at run time. Thus, deactivation of the process upon
modification.
• Centralization of security at the web service side, which causes a lot of overhead.
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A. Mourad 16
Another more dynamic approaches may be to enforce security
through policy languages like WSPL and XACML.XACML
Problem 2
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A. Mourad 17
Problem2• Large and complex policies lead to slower access
request/response time.
• Specifying security policies using these languages is difficult, error-prone and time consuming.
• Hidden conflicts that may arise due to the diversity of roles in policies that are difficult to locate and resolve.
• No verification processes to ensure policy correctness• Difficult to analyze and detect flawed policies due to complex
structure.• Multiple XACML party integration is very difficult.
• Usually they are enforced at the WS level
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A. Mourad
1- AspectBPEL: Dynamic Weaving based on Aspect-Oriented Programming
Original Code
…
...
f ();
Join Point
Weaved Code
...code1;code2;f();code3;code4;...
Weaver
Aspect
pointcut P = call (“% f(…)”);
advice P : before () {code1;code2;}
advice P: after () {code3;code4;}
Pointcut
Advice
18
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A. Mourad 19
1- AspectBPEL: Dynamic Weaving based on Aspect-Oriented Programming
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A. Mourad 20
AspectBPEL can solve the modularity and the security problems in the Web
services composition but…
1- AspectBPEL Limitations
Correctness
Deadlock-Free Original Behavior Maintainability
Complex Policies
AdaptabilityMoreover, the work in which AspectBPEL is presented does not provide
any methodology for verification before and after weaving
Conflict
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A. Mourad 21
Aspect aspect_name //Begin a New Aspect
BeginAspect
Before | After | Replace //Insertion Point
Activity_Type <activity_name> //Location Identifier
BeginBehavior
....Behaviorcode //Code to AddEndBehavior
EndAspect
Variable1 operator variable1_value connector Variable2 operator variable2_value …
1- Extended AspectBPEL
Priority priority_value
Activation_Condition
activation_condition_value
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A. Mourad 22
1- Extended AspectBPEL
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A. Mourad 23
1- Case Study
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A. Mourad 24
1- Case Study
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A. Mourad 25
1- Case Study
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A. Mourad 26
Only Authenticated users can get access to TBS services
Just in case the user books a complete package, the Discount will be applied
Encryption precedes Logging
Authentication “After” “receiveInput”
Discount, Encryption and Logging“Before” “Assign Payment Info To BWS”
1- Case Study
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A. Mourad 27
BPELProce
ss
BPEL2-
OWFNTool
PNMLFile
TINATool
KtzFile
LTLProper
ty
Result
1- Formal Verification Mechanism on BPEL
Original Behavior Maintainability
Deadlock-Free
Correctness
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A. Mourad 28
1- Formal Verification Mechanism on BPEL
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A. Mourad 29
1- Formal Verification Mechanism on BPELTable-1 Original Functionalities Maintainability Verification
Table-2 Deadlock-Free Verification
In the next state |
Always in the future |
Eventually | Alternative of OR |
Logical implication
Table-3 Correctness Verification
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A. Mourad 30
2- SBA-XACML Evaluation and AnalysisSBA-XACML Language
SBA-XACML Compiler
Analysis Report
XACML PolicySet
SBA-XACML Request
Response
XACML Request
SBA-XACML PolicySet
Policy Evaluation
Module
Policy AnalysisModule
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A. Mourad 31
2- SBA-XACML Syntax• A PolicySet (PS) is the top element of the based policy and is mapped to set-based as:
Element DefinitionPS PolicySetID PolicySet IDSP Set of PoliciesPR Precedence order between policiesPCA Policy Combining AlgorithmIPS Policy referenceOBLs Set of ObligationsTR Target
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A. Mourad 32
2- SBA-XACML Syntax• A Policy (P) is the middle element of the based policy and is mapped to set-based as:
Element DefinitionP Policy TypeID Policy IDSR Set of RulesPR Precedence order between rulesRCA Rule Combining AlgorithmOBLs Set of ObligationsTR Policy Target
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A. Mourad 33
2- SBA-XACML Syntax• A Rule (R) is the bottom element of the based policy and is mapped to set-based as:
Element DefinitionR Type: RuleID Rule IDRC Rule ConditionsTR Rule TargetRE Rule Effect
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A. Mourad 34
2- SBA-XACML Syntax
• A Request (Rq) is mapped to set-based as:
Element DefinitionRq Type: RequestSr Request SubjectsRr Request ResourcesAr Request Actions
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A. Mourad
2- XACML to SBA-XACML
35
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A. Mourad 36
2- XACML to SBA-XACMLXACML Request:
SBA-XACML Request:
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A. Mourad
2- SBA-XACML Evaluation Semantics (27 rules)
37
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A. Mourad
@1600 rules Avg. per 100k Run
Sun PDP 143 msXEngine 109 msSBA-XACML 31.62 ms
38
Synthetic Policy Evaluation
Real Policy Evaluation
@298 rules Avg. per 100k Run
Sun PDP 60 msXEngine 23 msSBA-XACML 8 ms
SBA-XACML is 7.5 and 2.8 times more efficient than Sun PDP and XEngine respectively.
SBA-XACML is 4.5 and 3.4 times more efficient than Sun PDP and XEngine respectively.
2- Experimental Results
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A. Mourad 39
2- Flaws Detection Semantics Flaws, Conflicts and Redundancy Detection
(4)
(3)
(2)
(1)
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A. Mourad 40
2- Flaws Detection Semantics Case Study PolicySet : PS1
Policy:P1 Policy:P2
Rule:R3Rule:R1 Rule:R4
Target (TR1):any subjectany resourceany action
Rule condition (RC1):Resource = withdraw
Rule effect (RE1):permit
Target (TR3):any subjectany resourceany action
Rule condition (RC3):Resource = deposit
Rule effect (RE3):permit
Target (TR4):any subjectany resourceany action
Rule condition (RC4):Resource = deposit &Subject = Joe
Rule effect (RE4):permit
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A. Mourad 41
2- Flaws Detection Semantics Case Study
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A. Mourad 42
Outline
• Project Overview• Security of Composite Services
• AspectBPEL• SBA-XACML
• Selfish Node Detection in VANET• Efficient Clustering Model• Cooperative Detection Model
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A. Mourad 43
oClustering & RoutingoMobility-based clustering algorithms such as DMAC and APROVE
focus on direction and speed to group vehicles.• However, mobility-based algorithms ignore the QoS metricso QoS-based clustering algorithms such as QOLSR and QoS-OLSR focus on
bandwidth and energy to group vehicles.• The QoS-based algorithms ignore the mobility constraints
o Securityo In reputation-based schemes, nodes monitor, detect, and then
declare another node to be misbehaving. This announcement is then broadcasted all over the network, leading to discard the misbehaving node from being used in all future routes.
• Limitations: ambiguous collision, false alarms, and non-cooperative decision
Problem
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A. Mourad 44
1
5
4
3
211
12
108
7 11
CH-2
MPR Cluster-head Normal Node
14
1 138
7
9
Cluster 1 Cluster 2
13
12
Notations
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A. Mourad 45
oVANET QoS-OLSR:o Extend the network lifetime while maintaining the Quality of
Serviceo Reduce the communications overheado Prevent the cheating during elections
oVANET-DSD:o Motivate the cooperationo Detect the selfish/misbehaving vehicles after elections
Approach
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A. Mourad 46
QoS Model
Cluster-heads election
MPR nodes Selection
VANET QoS-OLSR
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A. Mourad 47
Symbol Significance i A node in the networkQoS(i) Quality of Service Metric of node iBW(i) Available bandwidth of iN(i) Neighbors of iResidualDistance(i) ResidualDistance(i)= MaximumDistance-CurrentPosition(i)DistRatio(i) (ResidualDistance(i)/MaximumDistance)AvgSpeed AvgSpeed= Total distance/Total time = 2D/(t1 + t2).VelRatio(i) Velocity (i)/AvgSpeed
QoS(i) = BW(i) x N(i) x DistRatio(i)/VelRatio(i)
QoS Model
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A. Mourad 48
• QoS= Bandwidth x Connectivity x Distance/velocity• Propotional relation with the bandwidth:
more reliability• Propotional relation with the connectivity:
less percentage of MPRs & overhead• Propotional relation with the distance:
more stability• Inversely proportional relation with the velocity:
more & more stability
QoS Model
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A. Mourad 49
QoS=500
QoS=200
QoS=100
QoS=300
QoS=300
QoS=800
QoS=500
I am the cluster-head
I am the cluster-head
Ack message
Ack message
Cluster-Heads Election
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A. Mourad 50
1
5
4
3
2
6
11
12
108
11
CH-2ant1-1
8
MPR Node Cluster-head Node Normal Node
1 13
14
ant1
ant2
Encrypt QoS
Encrypt QoS
Decrypt QoS
Phermone(i)=QoS(i)-Route Time(i)
Node 6Node 1Node 8
1 8
QoS=200QoS=300
Route Time(1)= 10 Route Time(2)= 10
Phermone(2)=500-10=490
Phermone(1)=480-10=470
ant2-1
QoS=280
MPRs Selection
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A. Mourad 51
o Percentage MPRs: The number of elected MPR nodes.o Percentage of stability: current number of nodes in each cluster
divided by the previous number of nodes in ito End-to-End delay: the average number of hops needed to transfer data
between the source and the destinationo Packet Delivery Ratio: # of received packets/ # of sent packets by the
source.o Bandwidth Average Difference: it is the bandwidth difference
between the path having the maximal bandwidth value and the path currently selected.
Factors to Evaluate
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A. Mourad 52
A simulation area of 3000 x 1000 m
Simulation Area
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A. Mourad 53
Simulation Parameters
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A. Mourad 54
30 40 50 60 70 80 90 10040
45
50
55
60
65
70
75
80
Number Of Nodes
Per
cent
age
of s
tabi
lity
VANET QoS-OLSRQoS-OLSRClassical QOLSR
30 40 50 60 70 80 90 1000
10
20
30
40
50
60
Number Of Nodes
Per
cent
age
of M
PR
s
VANET QoS-OLSRQoS-OLSRClassical QOLSR
30 40 50 60 70 80 90 1000.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number Of Nodes
Pac
ket D
eliv
ery
Rat
io
VANET QoS-OLSRQoS-OLSRClassical QOLSR
30 40 50 60 70 80 90 1000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
Number Of Nodes
Ave
rage
Num
ber O
f Hop
s
VANET QoS-OLSRQoS-OLSRClassical QOLSR
Simulation Results
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oWill the vehicles follow this protocol or not?oQuestions on mind:
o Why should I pay time & resources serving as MPR for nothing?
o What is the return?
Selfish/Misbehaing Nodes Problem
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o Selfish nodes in VANET are rational nodes that prefer to over-speed or under-speed on the road instead of spending their resources and time without receiving return.oTypes of selfish nodes
During elections After elections
Definition of Selfish Nodes
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A. Mourad 57
30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Number Of Nodes
Per
cent
age
of M
PR
s
0% selfish nodes20% selfish nodes30% selfish nodes40% selfish nodes50% selfish nodes
30 40 50 60 70 80 90 1000
10
20
30
40
50
60
70
80
90
100
Number Of Nodes
Per
cent
age
of s
tabi
lity
0% selfish nodes20% selfish nodes30% selfish nodes40% selfish nodes50% selfish nodes
30 40 50 60 70 80 90 1001.8
2
2.2
2.4
2.6
2.8
3
Number Of Nodes
Ave
rage
Num
ber O
f Hop
s
0% selfish nodes20% selfish nodes30% selfish nodes40% selfish nodes50% selfish nodes
Impact of Selfish Nodes
![Page 58: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/58.jpg)
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Payment & Reputation
Network Services Distribution
Detection Mechanism
VANET- DSD: Cooperative Detection Model
![Page 59: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/59.jpg)
A. Mourad 59
1
5
4
3
2
11
12
108
7 11
CH-2
MPR Cluster-head Normal Node
14
1 138
7
9
13
12
QoS=500
QoS=450QoS=460QoS=510
6QoS=300 QoS=400
Payment(12)= QoS(12)-QoS(1)=500-460=40 Payment(8)= QoS(13)-QoS(8)=510-450=60
Reputation(1)=Reputation(1)+Payment(1)=100+60=160Reputation(12)=Reputation(12)+Payment(12)=100+40=140
13
76
Payment and Reputation
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1Reputation=109
2Reputation=130
3
Reputation=116
Available bandwidth=1000 Mb/s
Total Reputation=109+130+116=355
1Reputation Ratio=109/355
2Reputation Ratio=130/355
3Reputation Ratio=116/355
1BW share=109/355x1000
2BW share=130/355x1000
3
BW share=116/355x1000
Reputation and Network Service Distribution
![Page 61: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/61.jpg)
A. Mourad 61
Watchdogs Monitoring
Sharing
AggregationInformation
Dissemination
Detection Mechanism
![Page 62: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/62.jpg)
A. Mourad 62
1
5
4
3
2
1112
10
8
7 11
CH-2
MPR Cluster-head Normal NodeWatchdog
141 8
76
9
Packet P1
Overhear
Cluster 1 Cluster 2
13
Monitoring
![Page 63: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/63.jpg)
A. Mourad 63
1
5
4
3
2
11
10
8
7 11
CH-2
MPR Cluster-head Normal NodeWatchdog
141 8
76
9
Cluster 1 Cluster 2
1 is cooperative
1 is cooperative
1 is cooperative
1 is cooperative
1 is selfish
1 is cooperative
1213
Sharing
![Page 64: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/64.jpg)
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o The usefulness of Dempster–Shafer in representing and combining different
types of evidences coming from independent sources
o The fact that Dempster–Shafer represents uncertain evidences, which makes it appealing to model the ambiguity in the detection caused by the high mobility of vehicles and the channel collisions
o The good reputation of Dempster–Shafer in many critical fields like investigating crimes and diseases.
Aggregation – Why Dempster-Shafer
![Page 65: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/65.jpg)
A. Mourad 65
• Watchdog A (reputation = 0.9):• m1(C) = 0.9 (Vehicle 1 is cooperative)• m1(U) = 0.1 (watchdog 1 is uncertain)• m1(S) = 0 (M is selfish)
• Watchdog B (reputation = 0.8):• m2(C) = 0 (Vehicle 1 is cooperative)• m2(U) = 0.2 (Vehicle 1 is selfish)• m2(S) = 0.8 (watchdog 2 is uncertain)
Aggregation Example
![Page 66: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/66.jpg)
A. Mourad 66
1
5
4
3
2
1112
10
8
7 11
CH-2
MPR Cluster-head Normal NodeWatchdog
141 138
76
9
Cluster 1 Cluster 2
1 is cooperative 1 is cooperative
Information Dissemination
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Simulation Results
![Page 68: Dr. Azzam Mourad Assistant Professor Department of Computer Science and Mathematics Lebanese American University (LAU)](https://reader035.vdocument.in/reader035/viewer/2022062400/568168bd550346895ddfb2b8/html5/thumbnails/68.jpg)
A. Mourad 68
Related Publications• A Cooperative Watchdog model based on Dempster-Shafer for Detecting Misbehaving Vehicles.
Accepted in the Journal of Computer Communications, 2014, Elsevier• A Dempster-Shafer based Tit-for-Tat Strategy to Regulate the Cooperation in VANET using QoS-
OLSR Protocol. Accepted in the Journal of Wireless Personal Communications, 2013, Springer• Reputation-Based Cooperative Detection Model of Selfish Nodes in Cluster-based QoS-OLSR
Protocol. Accepted in the Journal of Wireless Personal Communications, 2013, Springer• XrML-RBLicensing Approach Adopted to the BPEL process of Composite Web services. In the
Journal of Service Oriented Computing, 7(3): 217-230, 2013, Springe• Common Weaving Approach in Mainstream languages for Software Security Hardening. In the
Journal of Systems and Software, 86(10): 2654-2674, 2013, Elsevier• New XACML-AspectBPEL Approach for Composite Web Services Security. In the International
Journal of Web and Grid Services, 9(2): 127-145, 2013, Inderscience• Vanet-QOLSR: QoS-based Clustering OLSR Protocol for Vehicular Ad hoc Networks. In the Journal
of Computer Communications, 36(13): 1422-1435, 2013, Elsevier• A Novel Aspect-Oriented BPEL Framework for the Dynamic Enforcement of Web Services
Security. In the International Journal of Web and Grid Services, 8(4): 361–385, 2012, Inderscience• A synergy Between Context-Aware and AOP to Achieve Highly Adaptable Web Services. In the
Journal of Service Oriented Computing, 6(4): 379-392, 2012, Springer
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Questions?Thank You…