A Low-Cost Method to Thwart Relay Attacks in Wireless Sensor Networks

Reza Shokri

Tutors: Panos Papadimitratos, Marcin Poturalski

29 January 2008

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Agenda

• Neighbor Discovery and Relay Attacks• Currently Proposed Defense Methods• Our System Model• A Low-Cost Method to Thwart Relay Attacks• Analysis and Simulation Results• Conclusion

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Neighbor Discovery

• Neighbor Discovery is the Building Block of Multi-Hop Communication in WSN.

• Security Requirements– Authenticity (Authenticating the neighbors)– Availability (Discovering all neighbors)– Correctness (Verifying the neighborhood relation)

• Threats– Impersonation Attacks– Denial of Service (e.g. Jamming Attack)– Relay Attack

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Relay Attack

• Relaying messages between two nodes in a way that: nodes believe they are neighbors while they are not.

• Placing a Relay Point in vicinity of BS, the attacker attracts nodes to route their packets through the Relay Channel.

• Having control over the channel, he can perpetrate powerful external attack on Fake Links.

A1

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Agenda

• Neighbor Discovery and Relay Attacks• Currently Proposed Defense Methods• Our System Model• A Low-Cost Method to Thwart Relay Attacks• Analysis and Simulation Results• Conclusion

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Currently Proposed Defense Methods

• Distance Bounding [BC93, HK05][BC93] Stefan Brands and David Chaum. Distance-bounding protocols, 1993.

• Location-based [HPJ03, SRB01][HPJ03] Y.-C. Hu, A. Perrig, and D.B. Johnson. Packet leashes: a defense against wormhole

attacks in wireless networks, 2003.

• Using Directional Antenna [HE04][HE04] Lingxuan Hu and David Evans. Using directional antennas to prevent wormhole

attacks, 2004.

• Connectivity-based [BDV05, MGD07][BDV05] Levente Buttyán, László Dóra, and István Vajda. Statistical wormhole detection in

sensor networks, 2005.

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Observations

• These solutions are– Impractical in wireless sensor networks because they

require sophisticated hardware or trustworthy external information

– Not resilient against strong adversaries.

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Agenda

• Neighbor Discovery and Relay Attacks• Currently Proposed Defense Methods• Our System Model• A Low-Cost Method to Thwart Relay Attacks• Analysis and Simulation Results• Conclusion

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IEEE 802.15.4 Channel Model

• The IEEE 802.15.4 standard addresses a simple, low-cost communication network that allows a wireless connectivity between devices with a limited power.

• Signal propagation of MicaZ, IEEE 802.15.4 compliant, mote modules (Equipped with CC2420 RF transceivers on 2.4 GHz Frequency band):

Transmission Signal Power (dBm)

Received Signal Power (dBm) at Distance d (m)

Path Loss1 (dBm) at Distance d (m)1. Path loss (or path attenuation) is the reduction in power density (attenuation) of an electromagnetic wave as it propagates through space.

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IEEE 802.15.4 Channel ModelReceived Signal Strength via Distance (on MicaZ)

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Network Model

• A static wireless sensor network, composed of tiny motes uniformly distributed in the field.

• Nodes are able to transmit with different power levels and can measure the received signal strength.

• Inspired from the channel characteristics, neighbors have following properties:– Channel Symmetry– Bidirectional Connection Transitivity– Signal Attenuation– Polygon Distance Plausibility

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Channel Symmetry

• For any pair of neighbors, the path loss is equivalent in both directions (because it is dependent to distance).

• In practice there is a Symmetry Error.• The difference between RSS in two directions

should be less than Symmetry Error.

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Bidirectional Connection Transitivity

• Noise Floor at s < Received Signal Power from v• Received Signal Power from v < Received Signal Power from u• If s can not hear u, maybe there is a selective relay attack in

between

Suspicious Case

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Signal Attenuation

• Clearly, based on the path loss model:

d0: The reference distance (usually 1m in low-power communication), is chosen to be at a distance at which the propagation can be considered to be close enough to the transmitter such that multi-path and diffraction are negligible and the link is approximately that of free-space.

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Polygon Distance Plausibility

• Distance between connected nodes should match to a polygon on a plane.

• Error in distance estimation will be considered.

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We use currently proposed Security Association (SA) establishment protocol.• SA establishment framework:

• After these (at most) three messages, nodes have established a shared key.

• We use in our protocol which stands for SA material.

S

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Adversary Model

• We look at the network from the attacker’s point of view.

• We define Victim Topology as two sets of nodes corresponding to two sides of the attack. Each node is a member of one set and its path loss to the adversary is its representative. {{PLA1M},{PLB1M,PLB2M}}

Set BSet AVictims

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Attacker Strategy

• Attacker Strategy represents how the attacker wants to deceive the victim network (for example by changing the signal power).

• A Successful Strategy is the strategy that the attacker can deceive the nodes and remains undetected in the presence of secure neighbor discovery protocol.

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Agenda

• Neighbor Discovery and Relay Attacks• Currently Proposed Defense Methods• Our System Model• A Low-Cost Method to Thwart Relay Attacks• Analysis and Simulation Results• Conclusion

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Protocol has two phases:Neighbor Discovery and Neighbor Verification.

• Neighbor Discovery (ND)– Nodes simply look for their neighbors and perform SA

establishment. – They check "Channel Symmetry" and "Signal

Attenuation" properties.

• Neighbor Verification (NV)– Nodes exchange their Neighbor Table and check the

"Bidirectional Connection Transitivity" and “Polygon Distance Plausibility” properties.

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ND Phase

• Consider u performs ND and v is one of its neighbors.

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NV Phase

• Check following properties in CheckPlausibility:– Polygon Distance Plausibility– Bidirectional Connection Transitivity

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Agenda

• Neighbor Discovery and Relay Attacks• Currently Proposed Defense Methods• Our System Model• A Low-Cost Method to Thwart Relay Attacks• Analysis and Simulation Results• Conclusion

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Finding Successful Strategy for the Adversary

• To fulfill the “Symmetry Property”:– Adversary adds a ∆Pi (dBm) to each packet he wants to

relay for node i.

– To maximize his chance, | ∆Pi - ∆Pj | should be minimized.

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What is the best ∆P?

∆P (Number of nodes covered by the signal)

∆P (Probability of violating the “Signal Attenuation” property)

For median values, attacker may violate the “Polygon Distance Plausibility” and “Bidirectional Connection Transitivity” properties.

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“Selective Relay Strategy” is not always a successful strategy.

• Can be detected by “Bidirectional Connection Transitivity” property.

• Moreover, if– Nodes randomly use different power levels for NV.– Each node has a different identifier for each power level.– Identifiers of nodes are disclosed to their legitimate

neighbors (after authentication).

• Then,– Attacker can not link between two messages coming

from a single node with different power levels (different identifiers).

– Can not have a correct deterministic selective relay.

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Examples of Attack DetectionViolating “Signal Attenuation” Property

• Victim Topology = {{45,70}, {50,80}}

• PL(d0)=40 (dBm)

• Minimum ∆P to cover all nodes is: 60 (dBm)

50 (dBm)

45 (dBm)

70 (dBm)

80 (dBm)

45-60+50 = 35 < 40 Impossible (Signal Attenuation)

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Examples of Attack DetectionViolating “Polygon Distance Plausibility” PropertyTriangle Case• Victim Topology = {{73}, {72,79}}• ∆P = 83 (dBm)

• Distances through relay channel:• 11 + 18.5 < 54

79 (dBm)

72 (dBm)

73 (dBm)

54 m

54 m11 m

18.5 m

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Examples of Attack DetectionViolating “Polygon Distance Plausibility” PropertyQuadrilateral Case

• Victim Topology = {{81,86},{83,89}}

• ∆P = 86 (dBm)

• Localization error using path loss: 20m

A1

A2

Ma Mb

B1

B2

40m(81dBm)

56m(86dBm)

51m

(85d

Bm

) 46m(83dBm)

67m(89dBm)

54m(86dB

m)

B1

B2

54m(86dB

m)51

m(8

5dB

m)

34m(79dBm)

69m(89dBm)

50m(85dBm)48m(84dBm)

Impossible to find a unique location for B2

A1

A2

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Simulation Model

• Victim Network Size: |A|=|B|= 1, …, 10• Nodes Power level: 0 dBm.• Attacker Transmission range: 80m• Nodes Transmission Range: 70m.• Localization error: 20m• All possible ∆P values checked for a large number of

topologies (randomly generated), considering the constraints of ND and NV phases.

• The probability of detection is the proportion of cases the attacker is detected by ALERT.

• The effectiveness of the attack is the average number of fake links the attacker can make, without being detected.

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Simulation Results

|A| = |B|

∆P

Detection Probability

Attack Success

Victim Network Size

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Agenda

• Neighbor Discovery and Relay Attacks• Currently Proposed Defense Methods• Our System Model• A Low-Cost Method to Thwart Relay Attacks• Analysis and Simulation Results• Conclusion

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Conclusion and On-Going Work

• We proposed a low-cost secure neighbor discovery protocol for wireless sensor networks.

• Our protocol is based on basic principles of wireless channel and geometry.

• We are implementing our protocol on real sensors to check its effectiveness in real situations.

• Challenges are calibration of receivers to reduce the “Symmetry Error” and tuning the path loss model to have more precise distance measurement.

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References

[BC93] Stefan Brands and David Chaum. Distance-bounding protocols. In Theory and Application of Cryptographic Techniques, 1993.

[BDV05] Levente Buttyán, László Dóra, István Vajda. Statistical wormhole detection in sensor networks. Lecture Notes in Computer Science, 2005.

[HE04] Lingxuan Hu and David Evans. Using directional antennas to prevent wormhole attacks. In NDSS, 2004.

[HK05] Gerhard P. Hancke and Markus G. Kuhn. An RFID distance bounding protocol. In SECURECOMM 2005.

[HPJ03] Y.-C. Hu, A. Perrig, and D.B. Johnson. Packet leashes: a defense against wormhole attacks in wireless networks. In INFOCOM 2003.

[MGD07] R. Maheshwari, J. Gao, and S. R. Das. Detecting wormhole attacks in wireless networks using connectivity information. In INFOCOM 2007.

[PPS+07] Panos Papadimitratos, Marcin Poturalski, Patrick Schaller, Pascal lafourcade, David Basin, Srdjan Capkun, and Jean-Pierre Hubaux. Secure neighborhood discovery: A fundamental element for mobile ad hoc networking. Accepted in IEEE Communication Magazine, 2007.

[SRB01] Chris Savarese, Jan M. Rabaey, and Jan Beutel. Locationing in distributed adhoc wireless sensor networks. In ICASSP 2001.