Hao Yang, Fan Ye, Yuan Yuan, Songwu Lu, William Arbaugh

(UCLA, IBM, U. Maryland)MobiHoc 2005

Toward Resilient Security in Wireless Sensor Networks.

Outline

Introduction and Background On resiliency of existing solutions LBRS Design Analysis and Simulation Results Discussions and Conclusions

Introduction

Target problems: Compromised nodes inside attacks Report fabric

ation attacks The compromised nodes forge nonexistent events that

cause both false alarms and resource waste

Existing solution and their problem Multiple parties endorse an legitimate event; en-route fi

ltering. Problem: Threshold breaks down.

Proposed approach: use location-based information to achieve resilience.

Assumptions

A Large scale sensor network that monitors a vast geographic terrain.

Size and shape of the terrain are known a priori

Sensor nodes are uniformly and randomly deployed in the terrain.

Once deployed, each node can obtain its geographic location via a localization scheme.

One resourceful sink with high survivability. Sink knows all keys

General En-route Filtering Framework

A node stores a set of symmetric keys. it uses one key to generate a Message Authentication Code (MAC) attached to an event report. It also uses its keys to verify the report forwarded to it. Each key has a unique index.

Set of symmetric keys: k1, k2, k3…

General En-route Filtering Framework

On event occurrence: A legitimate report must carry m distinct MACs. Multiple nodes sense the event and

collaboratively generate (one or more) reports with m MACs.

Report | index3 | MAC3

Report | index1 | MAC1

Report | index5 | MAC5

Report | index2 | MAC2

Report | index4 | MAC4

Report | index6 | MAC6

| index1 | MAC1Report | index3 | MAC3 | index4 | MAC4

General En-route Filtering Framework

Intermediate nodes:

Received Report

Check if it has m MACs

Check if it can verify the MACs

Is the MAC valid?

Forward packetDrop

No

No

No

Yes

General En-route Filtering Framework

Sink verification: Sink knows all keys, it can verify every MAC. Sink is the final guard

Outline

Introduction and Background On resiliency of existing solutions LBRS Design Analysis and Simulation Results Discussions and Conclusions

Interleaved Hop-by-Hop Authentication (IHA) Design parameter: m Each sensing cluster contains at least m+1 n

odes and a cluster head. Along the path, two nodes that are m+1 hops

away are associated by a pair-wise key. Threshold: m.

Interleaved Hop-by-Hop Authentication (IHA)

An Application Scenario

IHA Overview

Node initialization and deployment Each node has a unique id and should establish a

pairwise key with each of its neighbors Association discovery

Each node discovers the ids of all associated nodes Report endorsement

t+1 nodes collaboratively generate a report when an event is detected

Each participating node generates two MACs, one with the key shared with the BS, and one with the key shared with its upper associated node

CH head collects all MACs and attaches them to the report, forwarding to the BS

IHA Overview

En-Route Filtering Forwarding node verifies the MAC computed

by its lower association node; if success, it removes the MAC and computed a new one with the key shared with its upper association node

Base Station Verification BS contains a unique shared key with each

sensor

Summary of IHA

IHA verifies the reports in a deterministic and hop-by-hop fashion

Two major drawbacks in resiliency The protection breaks down when more than t

nodes along the path are compromised IHA relies on deterministic key sharing, which

results in high overhead due to dynamism Higher overhead to detect association nodes No definition on key establishment

Statistical En-route Filtering (SEF)

Global key pool is divided into m partition. Each node pre-loads with a few keys randomly chose

n from a single partition SEF is probabilistic

When an event occurs, detecting nodes jointly endorse the report with m MACs, each using a key in a different partition.

SEF assigns keys to nodes in a way that any intermediate node is able to verify the report with certain probability

Threshold: attackers obtain keys from m partition.

Outline

Introduction and Background On resiliency of existing solutions LBRS Design Analysis and Simulation Results Discussions and Conclusions

Location-Based Resilient Security (LBRS)

Terrain is divided into geographic grids and each cell is bonded with L keys.

Each node stores one key for each of its sensing cells.

Each node randomly chosen a few remote cells based on location information as its verifiable cells, and store one key for each of them.

Location-Based Resilient Security (LBRS)

Location-Based Resilient Security (LBRS)

A legitimate report is jointly generated by detecting nodes, and should carries m distinct MACs.

Intermediate nodes and sink verification processes are similar to SEF and IAH.

Two more new checking: All m distinct MACs should be bonded to one cell. Location attached in the report consistent with the

location of MACs

Location-binding key generation

Location-binding key generation: The terrain is divided into geographic grids and each cell is bounded with L keys.

How to construct a grid? How to derive keys based on the location info

rmation in a computationally efficient manner?

How to construct a grid

A virtual square grid is uniquely defined by two parameters: a cell size C, and a reference point (X0,Y0) (e.g., sink location).

Denote a cell by the location of its center, (X i,Yj), such that

How to derive keys

Preload each node with: cell size C, reference (X

0,Y0), master secret KI .

Once deployed, a node first obtains its geographic location through a localization scheme.

Derives keys during bootstrapping phase with

H() that is a one-way hash function. (Xi,Yj) is the location of the cell.

Location-guided key selection

A node defines an upstream region based on location information and only forward packet for its upstream region.

After defined upstream region, for each cell in its upstream region, select it as a verifiable cell with probability

d is the node’s distance to the sink, Dmax is the max distance between network edge and sink

Location-guided key selection

How to select upstream region and accommodate node failures? Designed to work with geographic routing

protocol. Upon moderate node failures, geographic

routing protocol find a closer detoured paths . Define beam width b. Use b and d (distance to sink) to define

upstream region.

Location-guided key selection

Benefits of LBRS

Randomized multiple compromised nodes are difficult to compromise a cell (oblivious attacks).

Damage is bonded to some local cells (smart attacks).

Trade off between storage and filtering power Location-guided key selection can reduce the

keys stored on one node and still achieve reasonable filtering power.

Outline

Introduction and Background On resiliency of existing solutions Design Analysis and Simulation Results Discussions and Conclusions

Parameter settings

Analysis—Filtering Effectiveness

One node compromised, with a distance to the BS d0 BS is in the center of the circular terrion

Detection Ratio: the percentage of forged reports being detected. Should be close to one.

Filtering Position: the number of hops a forged report can traverse before being dropped.

Analysis—Filtering Effectiveness

Analysis—Key Storage Overhead

Simulation

Platform: own simulator by Parsec language 30K nodes, 5Km x 5Km field, 100m x 100m

cell. Each simulation repeated 1000 times.

Simulation—Resiliency to random node compromise (oblivious)

Compromised nodes randomly scattered. How many cells will be compromised.

Simulation—Resiliency to random node compromise

Nc = Number of compromised nodes

Simulation—Filtering Power

Kc = number of compromised keys in a cell

Simulation—Delivery Ratio

Outline

Introduction and Background On resiliency of existing solutions Design Analysis and Simulation Results Discussions and Conclusions

Discussion

Prototype implementation: could all these fit into sensor nodes??

Platform: MICA2 Code size:

9358 bytes ROM, 665 bytes RAM Execution time: 100x100 cells

Bootstrapping: 2.8 sec MAC generation and verification: 10 ms

Discussion (Cont’)

Sensor deployment: Location information is known? Location information is required?

Routing Upstream region estimation is designed to work

with geographic routing protocols. They found some non-geographic routing

protocols (Directed Diffusion, GRAB) fit well with this model.

Require future study.

Conclusions If location is a required information, embedded

keys with locations seem to be obvious. Upstream region model is a good way to reduce

the key storage and still maintain the filtering power.

They did quite a bit of analysis and simulations to verify their claims.

Security setting is based on application scenario.