TRUST MANAGEMENT SYSTEM DESIGN FOR
THE INTERNET OF THINGS: A CONTEXTAWARE AND MULTI-SERVICE APPROACH
Yosra Ben Saied, Alexis Olivereau, Djamal Zeghlache,
Maryline Laurent
Presented by Ali Asgar Sohanghpurwala
INTRODUCTION
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Machine to Machine (M2M) and Internet of
Things (IoT) architectures becoming prevalent
Wireless Sensor Networks (WSNs) introduced
unattended wireless topologies with resource
constrained nodes
IoT expands on WSN requirements
 Wider architectures
 More heterogeneous
 Inconstant resource capabilities
 Increased autonomy
WHY DOES IOT NEED A TMS?
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Nodes expected to securely communicate with external Internet
nodes, but likely don't have resources to do it alone
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Collaboration needs to be controlled, to protect against attacks
Cr yptographic methods don't account for insider attacks
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Constraints such as computing power, battery life, limited bandwidth
Need to collaborate to meet this goal
Cooperative techniques for routing and security have been proposed in
literature
Cryptographically trusted nodes can lie, alter data, or selfishly refuse to
collaborate
Existing WSN and MANET insuf ficient for IoT
HOW IS TMS DIFFERENT FOR IOT?
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IoT nodes providing different services
assessed by same TMS
Non-malicious nodes may temporarily have
low capabilities
IoT nodes are highly heterogeneous
Node owned by multiple self-interest
communities
Complex malicious patterns arise with
coexistence of heterogeneous and selfconcerned nodes
ASSESSMENT OF PRIOR TMS WRT IOT
OVERVIEW
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Use past behavior to
determine task -specific trust
levels for each node
Eventually only the best
partners for a specific
ser vice are proposed to
requesting node
Fine-tune trust levels, even
in presence of malicious and
erroneous nodes
Geographically centralized
TM ser vers
Multi-phase approach
INITIALIZATION AND INFORMATION
GATHERING
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Initially all nodes are assumed trustworthy
Bootstrapping period is required to gather information
before results are trustworthy
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Trust manager speeds up process by targeting nodes and
inducing artificial interactions
Requesting node classifies behavior of assisting node
as positive or negative
Evaluations are stored in trust manager
Context under which evaluations are received is
important
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Aging, resource capacity, etc. of evaluated node
Execution time
REPORT INFORMATION
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Each report R ij
refers to jth report
regarding QoS for
assisting node P i
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Each report contains
the following
information:
ENTIT Y SELECTION
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When a node asks for assistance, the trust
manager returns a list of trustworthy assisting
nodes
Five steps:
1) Restrict set of proxies p i
2)Restrict the set of reports R ij for each proxy P i
3)Compute weights (w Rij) for each report R ij
4)Compute trust value T i for each proxy p i
5)Provide requestor with list of best suited proxies
ENTIT Y 1: RESTRICT SET OF PROXIES P I
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Select candidates based on service
requirements
Examples:
 Lightweight communication may require nodes
in same multi-cast group
 Signature delegation schemes may require
nodes dispersed in specific locations
 May require neighbors in radio range
ENTIT Y 2: RESTRICT SET OF REPORTS
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Find most meaningful reports for prospective
nodes
Ideal reports:
 Assisting node provided the same service
 Assisting node status was the same as it is now
It is likely that there won't be enough ideal
reports to judge the node p i in specific context
 We can calculate context similarity by
quantifying node capabilities and service
similarity
ENTIT Y 2:QUANTIFY PARAMETERS
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Quantifying node capability is easy:
 Percentage of Battery, CPU power,
Memory available
Service similarity isn't as straightforward
 Estimate service similarity based on
resource requirements
 Of measurable resources, energy
consumption is recommended by authors
ENTIT Y 2: CONTEXT SIMILARIT Y
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Report R ij sent by all
nodes j, regarding
interactions with
node P i contains:
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S j – service provided
by
C j – capability
N j – Note
Try to match with
target values:
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S target – Current service
in request
C target – Current P i
capability
ENTIT Y 2: CONTEXTUAL DISTANCE
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dS max , dC max - tolerance of selection mechanism for capability
and ser vice measurements
First term represents distance from center of (( S target ,C target ),
dS max , dC max ) ellipse
Node that behaves well for expensive ser vice, is likely to behave
well for less demanding ser vice
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Second term represents distance between R ij and (S max , 0)
Node per forming well for low -demand ser vice, doesn't mean it
per forms well in demanding ser vice
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Second term represents distance between R ij and (0, C max )
ENTIT Y 2: D IJ ILLUSTRATION
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Positive report close to ( S max ,
0) means node per formed
well for expensive ser vice
while near min capacity
Negative Report close to (0,
C max ) means that node
per formed poorly for simple
ser vice, while at max
capacity
Any report close to center of
target ellipse is ver y similar
Retained report R ij should
have d ij such that:
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d ij (R ij , R Target ) < t, where:
ENTIT Y 2: EXAMPLE
ENTIT Y 2: EXAMPLE CONT.
ENTIT Y 3: COMPUTE WEIGHT FOR EACH
REPORT
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Weight of each report (w ij ) determined by contextual
distance (d ij ) and age (t now - t j )
λ,θ are parameters in range [0,1] expressing 'memory'
of the system
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θ (resp. λ) is adjusted according to expected rapidity of change
Lower θ (resp. λ) indicates lower importance for past reports
(resp. more contextually distant reports)
s = ½ * (N 2 j -N j ), where N j is the score given by witness
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s = 1 when score is -1, and 0 when score is 0,1
weight of negative score is doubled compared to positive or
neutral scores
ENTIT Y 4: COMPUTE TRUST VALUE FOR
EACH PROXY
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T i is trust value for proxy p i
QR j is the quality of recommendation of witness
node j
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trustworthiness score based on accuracy of past
reports
Ranges between -1 and 1
w Rij is weight from previous slide
ENTIT Y 5: PROVISION BEST RATED
PROXIES OF P I
Securely send list of best
rated nodes to requestor
 Finally done with Entity
selection
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TRANSACTION AND EVALUATION
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Client node relies on list of trusted proxies
provided by Trust Manager to select partners
Sends positive or negative score for each
partner to TM
Evaluation technique depends on service
provided
 Could be direct observation, or could solicit
feedback from peers
Received reports should take into account node
credibility
LEARNING
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Learning phase qualifies
system as a cognitive
process
In security scenarios:
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Adaptive security systems
dynamically react by
applying new security
policies in reaction to
environment change
Cognitive security
introduces learning step.
Assessment of enforced
action eventually modifies
system behavior so a
different action may be
taken next time.
LEARNING STEPS
1)Update witness nodes'
qualities of recommendation
2)Update of assisting nodes'
reputation levels
LEARNING 1: UPDATE QUALIT Y OF
RECOMMENDATION
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Simple Concept
 Decrease QR score if witness gave a 'bad' score
to a 'good' node
 Increase QR score if witness gave a 'good' score
to a 'good' node
Use weighted average
 avoids excessive variations of QR
 allows precise choice of extent to which QR
must be oriented towards 1 (good
recommender), 0 (non-usable data), and -1 (bad
recommender)
LEARNING 1: QR DIRECTION
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Let X be a witness node who evaluated node P, which later
provided a ser vice to node F
Node F sends report R F with score N {-1 ,0,1}
TM uses F's report to update QR score of each recommender
System retrieves n stored QR scores for all witness nodes
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X has for example QR x (QR 1 ,...,QR n-1 , Qr n )
System extracts note N from R F and retrieves w Rx corresponding
to R X
QR X F represents direction that QR for X should evolve
r = 1 if X and F agree on rating, r= -1 when they are opposite, and
r=0 when they are of f by 1
C F is weight of r, increases when weight of X's previous report is
high, increases if F is a good recommender
LEARNING 1: WEIGHTED AVERAGE
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QR i represents X's rec. history
C i is respective weight for X's rec. history
θ represents 'memory' factor of system
Negative N_QR means X is reporting opposite of
actual service quality
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Instead of discarding X's ratings, consider the opposite!
LEARNING 2: UPDATE ASSISTING NODES'
REPUTATION LEVELS
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Reputation distinct from trust
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Trust measures ability of node
to perform specific task
Reputation refers to overall
trustworthiness of node in the
system
Combine QR (QR Fj )and ser vice
ratings (N Fj ) with age-based
weighting factor
TM recalculates reputation
af ter each interaction
Nodes with low -reputation are
added to blacklist
TEST SIMULATION
SIMULATION PARAMETERS
QR EVOLUTION
QR EVOLUTION CONT.
ATTACK RESILIENCE
ATTACK RESILIENCE CONT.
CONCLUSION
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Presented generic, context-aware TMS for IoT
Dynamic trust scores assigned to nodes based
on node status, and required function
Independent score given for Quality of
Recommendation
QR score is adjusted through learning phase
System withstands several classes of attacks