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Detecting Phantom Nodes in Wireless Sensor NetworksPowerPoint Presentation

Detecting Phantom Nodes in Wireless Sensor Networks

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Detecting Phantom Nodes in Wireless Sensor Networks

- Joengmin Hwang
- Tian He
- Yongdae Kim
- Department of Computer Science, University of Minnesota, Minneapolis
- Infocom 2007
- Slides by Alex Papadimitriou

Detecting Phantom Nodes in Wireless Sensor Networks

- Security attacks become possible if malicious nodes could claim fake locations that are different from where they are physically located.
- Propose a secure localization mechanism that detects the existence of these nodes, phantom nodes.
- Most approaches depend on a few trusted entities (nodes or anchors), requiring at least the majority of these entities are not compromised.
- Based on a local map, a visual representation on the locations of neighbors of a node, which can be constructed correctly by verifying all location claims of its legitimate neighbors and filtering out phantom nodes generated by attacks.

Detecting Phantom Nodes in Wireless Sensor Networks

- Benefits:
- A node’s compromised decision does not propagate to affect other nodes’ decisions.
- Much less information exchange is required leading to less energy consumption.

- Major Contributions:
- Two rules to prevent phantom nodes generating consistent ranging claims.
- The approach recovers a local map agreed by the majority of consistent information.
- The approach can use any ranging technique, not specially requiring distance bounding technique for location verification.

Detecting Phantom Nodes in Wireless Sensor Networks

- Assumptions:
- Bidirectional channels
- Reasonable network density
- Two dimensions

- The design only allows a node to claim about its distancesto other neighboring nodes, not its own location.
- Therefore the phantom node needs to fake a set of distances to all of its neighboring nodes. Without the location information of the neighboring nodes, it is hard for an attacker to generate a set of consistent ranging values, and hence to fake itself into a different physical location.

Detecting Phantom Nodes in Wireless Sensor Networks

Fig.1a: attacker D at the location p obtains three ranging distances in the 2-D space from three honest nodes A, B and C. It can only conclude that A, B and C are located at the edges of three concentric circles centered at p. To claim a different physical location

p′ within the 2-D space, the attacker D needs to fake three different ranging distances that are consistent

- Two rules:
- Accepting only ranging claims, not location claims.
- Hiding the location information during the ranging phase.

Detecting Phantom Nodes in Wireless Sensor Networks

As shown in Figure 1a, to move from the position p to p′, the attacker D needs to claim two shorter ranging distances to Nodes B and C, but a longer ranging distance to Node A; However in case of Figure 1b, the attacker D needs to claim the opposite. Since the locations of A, B and C are unknown, the attacker cannot decide which claim to make.

- We note that a sensor network normally has a high node density (>>10), which makes a consistent ranging claim practically impossible without the neighbors’ location information.

Detecting Phantom Nodes in Wireless Sensor Networks

- A set of nodes is consistent if they can be projected on the unique Euclidean plane, keeping the measured distances among themselves.
- Two main phases: distance measurement phase and filtering phase. In the first phase, each node measures the distances to its neighbors. In the second phase, each node projects its neighboring nodes to a virtual local plane to determine the largest consistent subset of nodes. After the completion of the two phases, each node establishes a local view without phantom nodes.

Detecting Phantom Nodes in Wireless Sensor Networks

- Distance measurement phase:
Each node u measures the distances to neighbors and disseminates these measurements back to its neighbors.

- Node u first measures distance dui to each neighbor i.
- Node u then announces the measured distances. The announcement message includes id of the node u, id of the node I and distance measurement to i by u. Note that even when u knows about its location, it should not disclose it in this phase.
- When neighbor i announces its measured distance to its neighbor j, u collects dij. U collects neighbors’ announcements on the measured distances to their neighbors.
- After collection, node u compares the data. For each collected distance, if dij = dji, it is included in the filtering phase.
- It is possible that an attacker holds the announcements before it collects all the ranging information, and then calculates the relative locations of the honest nodes. Consequently, this attacker could fake a set of consistent range claims. To prevent such type of attack, we require each node announces one distance at a time in a round robin fashion within the neighborhood. This can be achieved by using pairwise ranging techniques [15].

Detecting Phantom Nodes in Wireless Sensor Networks

- Filtering phase:
- Initially, the node v picks up two neighbors i and j randomly as pivots.
- Using the node v as the origin, the neighbors i and j and three distance information among v, i and j, the local coordinate system is constructed. In the node v’s coordinate system, we use a graph G(V,E) to construct a consistent subset.
- The update process of the graph G is as follows: The location of the neighbor k is determined on the local coordinate system L by trilateration [16] from three nodes v, i, j with measured distances dkv, dki and dkj .
- After projecting all the neighbors on L, the distance between the projected neighbors is compared with the measured distance. For any two nodes i and j the distance dij = |pi − pj | is calculated from the projected location on L. If |dij − dij | ≥ ε, the edge between i and j is not included in E.
- The largest connected set V that contains node v is regarded as the largest consistent subset in the speculative plane L. This filtering procedure is done iter times (iter is a key parameter discussed later), and the cluster with the largest size is chosen as a final result.

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