Security of Sensor Networks

1
Security of Sensor Networks

• Tanya Roosta
• TRUST Seminar
• UC Berkeley, November 9, 2006

2
Overview

• Taxonomy of attacks on sensor networks
• Convergence analysis of Reweighted-Tree
  sum-product algorithms
• Time synchronization security
• Reputation system for tracking
• Game theory

3
Overview

• Taxonomy of attacks on sensor networks
• Convergence analysis of Reweighted-Tree
  sum-product algorithms
• Time synchronization security
• Reputation system for tracking
• Game theory

4
Background on Sensor Network

• Wireless networks consist of a large number of
  motes
• self-organizing, highly integrated with changing
  environment and network
• Highly Constrained resources
• processing, storage, bandwidth, power
• Facilitate large scale deployment
• Health care
• Surveillance
• Critical infrastructure

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Motivation

• Sometimes deployed in hostile environment, and
  have random topology
• Vision is to integrate sensors into critical
  infrastructure, such as wireless Supervisory
  Control And Data Acquisition systems (SCADA)
• Traditional security techniques can not be
  applied because

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Challenges Unique to Sensor Networks

• Random Topology
• Secure aggregation
• Context privacy PMRSSW06
• Scalability of trust/key management schemes
• Power and computation efficiency

PMRBSSW06 Sameer Pai, Marci Meingast, Tanya
Roosta, Sergio Bermudez, Shankar Sastry, Stephen
Wicker. Privacy in Sensor Networks A Focus On
Transactional Information. Under submission to
IEEE Security and Privacy Magazine
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Security Attacks on Sensor Networks

• Need to have a comprehensive taxonomy of security
  and confidentiality attacks on sensor networks to
  describe RSS06
• Attackers goal
• Trust model
• Security requirements
• Various types of attacks

• RSS06 Tanya Roosta, Shiuhpyng Shieh, Shankar
  Sastry. "Taxonomy of Security Attacks on Sensor
  Networks". IEEE International Conference on
  System Integration and Reliability Improvements
  2006

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Attackers Goal

• Eavesdropping (outsider attacker)
• Disruption of applications (insider attacker)
• Subverting a subset of sensor nodes (insider
  attacker)

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Trust Model

• There is usually a central base station that
  gathers all the data reported by the sensor nodes
• Only trust assumption the base station is
  trustworthy
• No other trust requirement is placed

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Security Requirements

• Confidentiality
• Authentication
• Integrity
• Freshness
• Secure Group Management
• Availability
• Graceful degradation

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Cryptography

• Cryptography is the first line of defense
• Cryptography helps with message integrity,
  authentication, and confidentiality
• TinySec symmetric key cryptographic algorithm
• TinyECC Elliptic Curve Cryptography (ECC)
• Cryptography can not solve all the problems of
  security in sensor networks

12
Security Attacks

• Attacks can be categorized into RSS 06
• Attacks on the sensor mote
• Attacks on the protocols and applications

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Attacks on the Sensor Mote

• Non-invasive The embedded device is not
  physically tampered with
• Side-channel attack
• Invasive Reverse engineering followed by probing
  techniques
• Extract cryptographic keys
• Exploit software vulnerabilities
• Memory access control

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Attacks on Protocols/Applications

• Denial of service
• Traffic analysis
• Time synchronization
• Key management protocols
• Data aggregation protocols
• Comprehensive list in RSS06

DOS
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Overview

• Taxonomy of attacks on sensor networks
• Convergence analysis of Reweighted-Tree
  sum-product algorithms
• Time synchronization security
• Reputation system for tracking
• Game theory

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Graphical Models

• In probabilistic graphical models, the nodes are
  random variables, and arcs (or lack of them)
  encodes the conditional independence of these
  random variables

• Specify a joint
  probability distribution among random variables

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Graphical Models in Sensor Networks

• Graphical models useful for distributed fusion in
  sensor networks CCFIMWW06
• Well-suited for sensor network structure
• Scalable inference algorithm, new message-passing
  algorithms
• Parallel message-passing

CCFIMWW06 M. Cetin, L. Chen, J. W. Fisher, A.
T. Ihler, R. L. Moses, M. J. Wainwright, A.
Willsky. Distributed Fusion in Sensor
Networks. IEEE Signal Processing Magazine, July
2006.
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Inference on Graphical Models

• Calculating posterior marginals is NP-hard
• Junction Tree algorithm finds exact marginals,
  but is computationally expensive
• Standard Belief Propagation (BP) is used as an
  approximate inference algorithm

BP Equation
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Tree-Reweighted Sum-Product Algorithm

• TRW is a broader class of approximate inference
  algorithms
• Message adjusted by edge-based weights
• The weights are ?ts20,1
• Computational complexity identical to BP
• ? 1 recovers the standard BP

WJW05 M. J. Wainwright and T. S. Jaakkola and
A. S. Willsky. "A new class of upper bounds on
the log partition function"IEEE Trans. Info.
Theory, 2005.
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Advantages of TRW

• For suitable choices of ?, TRW, in sharp contrast
  to BP, always has a unique fixed point for any
  graph and any dependency strength
• Additional benefit
• Message-passing updates tend to be more stable
• Faster convergence rate

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TRW in Sensor Networks

• TRW can be used in sensor networks CWCW03
• TRW and security
• Compromised nodes give faulty updates
• Need to understand
• How much of an effect the faulty updates will
  have on the estimation
• How the characteristics of the fixed points of
  TRW are changed

CWCW03 L. Chen, M. J. Wainwright, M. Cetin, A.
S. Willsky. Multitarget-Multisensor Data
Association Using Tree-Reweighted Max-Product
Algorithm. SPIE AeroSense Conference, 2003.
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Convergence Analysis of TRW RW06

• The objective is to analyze the convergence of
  the family of reweighted sum-product algorithms
• We assume that the true messages are fixed
  points of the algorithm
• The messages are perturbed by some amount

RW06 Tanya Roosta, Martin J. Wainwright.
"Convergence Analysis of Reweighted Sum-Product
Algorithms. Submitted to IEEE International
Conference on Acoustics, Speech, and Signal
Processing (ICASSP)
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Convergence Analysis RW06

• W.L.O.G restrict attention to the case of
  pair-wise cliques

?st

• The distribution defined on this graph is
• Analyze homogeneous and non-homogeneous models

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Homogeneous Model

• ?st ?, ?s ? for all edges and all nodes
• Let ddegree of the nodes
• If ?d-1 ? 1, then we are guaranteed uniqueness
  and convergence of the updates
• If ?d-1 1 , the update equation may have more
  than one fixed point, depending on the choice of
  ? and ?
• Proof

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d4
?critical
?
?

• Plot of the appearance of multiple fixed points
  versus ? and ?

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Non-Homogeneous Model

• In the general model, convergence analysis is
  based on establishing, under suitable conditions,
  the updates specify a contractive mapping in the
  l1 norm, i.e.

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Simulation Results

• ? uniform from 0.05,0.5, edge potentials ?st,
  uniform from 0.01,1, and different values for ?
• Number of nodes between 49-169
• Plot of log zm-z1 vs. the number of iterations
  (m)

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More figures
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Ongoing and Future Work

• The convergence condition is somewhat
  conservative
• Requires the message updates be contractive at
  every node of the graph
• We like to have an average-case analysis
• Require that updates be attractive in an average
  sense

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Overview

• Taxonomy of attacks on sensor networks
• Convergence analysis of Reweighted-Tree
  sum-product algorithms
• Time synchronization security
• Reputation system for tracking
• Game theory

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Why Need Time Sync.?

• Sources of error in time are
• Clock skew the difference in the frequencies of
  the clock and the perfect clock
• Clock offset the difference between the time
  reported by a clock and the real time

Time sync.
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Effect of Time Sync. Attacks

• Time sync. protocols are vulnerable to security
  attacks
• Effect on applications/services MRS05
• Shooter Localization
• TDMA-based Channel Sharing
• Flexible Power Scheduling
• TDMA-based MAC protocol
• Estimation
• Authenticated Broadcast (?Tesla)

MRS05 Mike Manzo, Tanya Roosta, Shankar Sastry.
Time Synchronization Attacks in Sensor
Networks. The Third ACM Workshop on Security of
Ad Hoc and Sensor Networks 2005
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Time Sync. Protocols in Sensor Network

• Three general categories
• Reference Broadcast Synchronization (RBS)
• TPSN
• Flooding Time Synchronization Protocol (FTSP)
• In MRS05 attacks and possible countermeasures
  for each time sync. protocols was explained

Description
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FTSP

• FTSP uses reference points for synchronization
• Reference point (globalTime, localTime)
• globalTime time of the transmitting node
• localTime time of the receiving node
• The receiving node uses linear regression on 8
  reference points to find offset and skew

Detail
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Attacks on FTSP RS06

• A compromised node can claim to be the root node
• The compromised root sends false updates, which
  will get propagated in the network
• Every node accepting the false updates calculates
  false offset and skew

RS06 Tanya Roosta, Shankar Sastry. Securing
Flooding Time Synchronization Protocol in Sensor
Networks". Workshop of 6th ACM IEEE Conference
on Embedded Software
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Proposed Countermeasures RS06

• Secure leader election mechanism
• distributed coin-flipping algorithms (use
  cryptographic commitments)
• Using redundancy
• Instead of LS on one neighbor, run LS on multiple
  neighbors and take the median
• Run LS on multiple random subsets of data
• Using robust estimators Least Median of Squares
  (LMS)

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Future work

• Experiments
• Implementing the attacks
• Analyze the effect on the tracking application
• Implement some of the countermeasures
• Time line 6 months

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Overview

• Taxonomy of attacks on sensor networks
• Convergence analysis of Reweighted-Tree
  sum-product algorithms
• Time synchronization security
• Reputation system for tracking
• Game theory

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Reputation System

• Reputation systems have been used in online
  ranking systems
• They have proven useful as a self-policing
  mechanism
• In GS04 the authors propose extending this
  framework to sensor networks

GS04 Saurahb Ganeriwal, Mani Srivastava.
 Reputation-based framework for high integrity
sensor Networks. Proceedings of the 2nd ACM
workshop on Security of ad hoc and sensor
networks, 2004.
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Reputation System in Sensor Network

• No unifying way to design the watchdog
  mechanism
• Application dependent

GS04
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Reputation System for Tracking RMS06

• We designed a reputation system for the tracking
  application
• Tracking is fundamental in sensor networks
• Surveillance
• Pursuit Evasion Games
• Focused on Hierarchical Multi-Object Tracking
  Algorithm (MCMCDA)

RMS06 Tanya Roosta, Marci Meingast, Shankar
Sastry. "Distributed Reputation System for
Tracking Applications in Sensor Networks". In
proc. of International Workshop on Advances in
Sensor Networks 2006
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MCMCDA

• The input
• a set of data indexed by time
• The output
• the association of the observed data with object
  tracks
• The tracking algorithm has two phases
• Data Fusion
• Data Association

ORS04 S. Oh, S. Russell, and S. Sastry.
Markov Chain Monte Carlo Data Association for
General Multiple-Target Tracking Problems. IEEE
International Conference on Decision and Control
(CDC), 2004.
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Example
ORS04

• Figure (a) shows the observed data indexed by
  time,
• Figure (b) shows the tracks that were formed
  based on the maximum likelihood function

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MCMCDA ORS04

• Nodes equipped with motion detection sensors
• Sensor model

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Data Fusion

• In each local neighborhood, the node with the
  highest signal strength declares itself to be the
  leader
• All the other nodes in the neighborhood send
  their observations to this leader
• The leader aggregates the data

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Data Association

• Each leader sends the fused observation to the
  closest super-node
• Super-node send their gathered fused observations
  to the base station
• Base station uses Markov Chain Monte Carlo (MCMC)
  to associate the fused data by maximizing the
  posterior of the track, given the observations

Formula
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Possible Attacks RMS06

• Adversary physically captures a subset of the
  sensor nodes
• Compromised nodes send faulty observations to the
  leader

• Results in wrong fused observations and formation
  of non-existent tracks for the moving objects

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Attacks Not Considered

• We did not allow the compromised nodes to claim
  to be the leader
• This problem could be solved using standard
  distributed coin-flipping algorithms using
  cryptographic commitments
• At the central level, we need to use statistical
  methods that would filter out the faulty
  observations coming from the compromised leaders

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Reputation System RMS06

• The nodes do not share their reputation table
• At this point, we only use first hand
  observations for updating the reputation
• Each node updates the reputation of its neighbors
  only when it becomes the leader
• The reputation is a value in 0,1

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The Algorithm RMS06

• Leader node gathers all the observations from its
  neighbors
• It chooses m subsets of the observations
• The members of each subset are chosen randomly
  from among all the neighbors
• The leader computes the fused observation for
  each subset ( )

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The Algorithm (cont.)

• is the accumulated reputation of the jth
• neighbor at node i up to time t-1
• The leader finds the median of
  where i 2 1,,m

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Reputation Assignment RMS06

• The median value of the estimated location is the
  trusted value (mtrust) and the nodes in the
  corresponding subset are trusted nodes (Strsut)
• There are two counters (?ij , ?ij) for
  instantaneous reputation
• ?ij positive reputation
• ?ij negative reputation

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Reputation Assignment (cont.)

• Nodes in Strust receive an instantaneous
  reputation of (1,0)
• For the rest of the neighbors, the leader picks
  one node, sij, at a time and add it to the subset
  Strust and recalculates the location estimation
• Call the result of this calculation

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Reputation Update RMS06

• T is a threshold to determine how far can
  be pulled away from the median mtrue
• T has to take the normal level of observation
  noise into account

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Reputation Aggregation RMS06

• Instantaneous reputations are aggregated to
  calculate the cumulative positive and negative
  reputation (rijt, sijt)
• Discounting factor, ?, is used to guarantees old
  reputations will be gradually forgotten
• The reputation is aggregated using

Beta function
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Simulation

• The surveillance region is a square grid of size
  50m x 50m
• There is one node placed at each corner of each
  square
• The number of objects we want to track is ni
• The sensing range Rs is set to 1.5m

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Simulation (cont.)

• The noise represented by a Gaussian standard
  distribution N(0,1)
• Tested different scenarios
• Example the number of compromised nodes is fixed
  and the sensing radius is varied from 1.5m to 3m
• T 0.4, m4, and s3
• Metric the average error in the number of tracks
  estimated by the algorithm compared to the actual
  number of tracks

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250 compromised nodes, varying sensing radius
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Qualitative Comparison
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Future Work

• Extend the observation model to include
  probability of compromised nodes using mixture
  models RMG06

RMG06 Tanya Roosta, Mubaraq Mishra, Ali
Ghazizadeh. Robust Detection and Estimation in
Ad-Hoc and Sensor Networks. IEEE International
Conference on Mobile Ad-hoc and Sensor Systems,
2006
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Overview

• Taxonomy of attacks on sensor networks
• Convergence analysis of Reweighted-Tree
  sum-product algorithms
• Time synchronization security
• Reputation system for tracking
• Game theory

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Clustering Game

• Setup
• There are a number of clusters K
• The adversary knows what is being observed
• The adversary can not observe what the other
  adversaries are doing (no collusion)
• The nodes are monitoring temperature (example)
• What is the optimal compromised node placement
  within the clusters to cause the most amount of
  damage?

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Good node
Compromised node
Which distribution of the compromised nodes has
the most affect on the final estimation at the
center?
More Game Theory
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Conclusion

• Security in sensor networks is crucial to
  successful deployment
• In this talk
• proposed a taxonomy of security attacks
• Gave convergence results for TRW
• Described attacks on time sync. Protocols and the
  effect on different application
• Developed a decentralized reputation system for
  tracking
• Use of game theory to formulate security attacks

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Thank you!
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Effect on Estimation (Example)

• state of a discrete-time controlled process
• Given the measurement

• Back

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Reputation and Beta Function

• The sequence of observations can be considered as
  a sample from a binomial distribution, i.e. a
  sequence of independent coin tosses, with a bias
  parameter P
• To be clear, the head corresponds to an honest
  node and the tail corresponds to a compromised
  node, and the bias is the overall reputation of
  the node
• We can estimate the rating of a node using
  Bayesian parameter estimation of the binomial
  distribution
• Back

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Reputation and Beta Function (cont.)

• The posterior probability of binary events is
  most accurately represented by the Beta
  distribution
• Beta distribution is a two parameter distribution
  with parameters a and b
• Parameter a measures the number of successes
  (rijt) and b measures the number of failures
  (sijt)
• The overall reputation is modeled as the expected
  value of the Beta distribution

Back
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Proof

• Message updates are characterized by
• Taking the derivative of F(z,?, ?, ?) will give
  the rate of convergence

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Proof (cont.)
?
Back
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Robust Detection

• The goal is to detect compromised/faulty nodes

• The lying behavior could be
• Static unchanging behavior
• Dynamic changing liars
• Dynamic colluding liars
• We can model each one of these cases using a
  Hidden Markov Model

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Problem Formulation

• The nodes make an observation according to
• No notion of time in our problem setup, i.e. the
  nodes collect all their observations, and then
  the detection is performed

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Problem Formulation

• Expectation Maximization (EM) framework is used
  to find the parameters (probability of a the node
  lying and the detection value)
• We maximize the log likelihood based on the lying
  behavior we are considering (which affects the
  hidden parameters)

Back
74
RBS
In RBS a reference message is broadcast to two
receivers and the receivers synchronize their
respective local clocks to each other

• A transmitter broadcasts m reference messages
• Each of the n receivers record their local
  received time
• Receivers exchange their local times.
• Each receiver calculates its phase offset as the
  LS linear regression of the phase offsets

Back
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TPSN

• TPSN creates a spanning tree of the sensor
  network
• Each node finds the clock drift and propagation
  delay, using

Back
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(No Transcript)
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Back
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Denial of Service Attacks

• Denial of service attack concerns any attack that
  diminishes the networks capacity to perform its
  function
• Denial of service attacks can be carried out at
  any of the layers of the communication stack

Back
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Denial of Service Attacks
Back
WS02 A. Woods, J. StankovicDenial of Service
Attacks in Sensor Networks. IEEE Computer,
35(10)54-62, October 2002
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Ordinary Belief Propagation

• Message and belief updates

Back
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LMS
Back
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Time Synchronization

• Time synchronization protocols provide a
  mechanism for synchronizing the local clocks of
  the nodes in a sensor network
• Two ways to synchronize the clocks
• Synchronization to accurate real time
• Relative synchronization for ordering of the
  events
• Clock model

Back
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FTSP (cont.)

• Offset
• Skew

Back
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Example
Back
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Data Association (cont.)

• Maximizing the posterior of the track, given the
  observations, Y

Back
zt number of objects terminated at time t, at
number of new objects at time t, dt the number of
detections, ft the probability of false alarms,
?f the false alarm rate, ?b the birth rate of a
new object, pz the probability of an object
disappearing, and pd the probability of detection.
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Attack Trees

• Attack trees provide a formal, methodical way of
  describing the security of systems, based on
  varying attacks
• The tree can also be used to determine where a
  system is vulnerable, and weigh the benefits of
  different countermeasures against one another
• We want to develop an efficient attack tree for
  sensor networks
• An example based on the taxonomy paper

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(No Transcript)
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Routing Game1

• The power consumption in routing has been modeled
  as a dynamic Bayesian game among the N nodes of
  the network
• Uses action history hi(tk)(si(t0), , si(tk-1)
  )
• This Bayesian game has a Nash equilibrium
  solution, but the solution strategy has not been
  explicitly found

1-Petteri Nurmi. Modelling Routing in Wireless
Ad Hoc Networks with Dynamic Bayesian Games.
IEEE SECON, 2004
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Future Work

• What are the actual solutions to this Bayesian
  game (if we can explicitly solve for the
  equilibrium)?
• Affect of memory/action history length on the
  outcome of the Bayesian game
• Learning the reputations of nodes dynamically
  using the solution to the Bayesian game
• Time line 1 year

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TRW Message Update
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