04-07-2012, 03:50 PM
Fast Detection of Replica Node Attacks in Mobile Sensor Networks Using Sequential Analysis
[attachment=26714]
INTRODUCTION
Advances in robotics have made it possible to develop a
variety of new architectures for autonomous wireless networks
of sensors. Mobile nodes, essentially small robots with sensing,
wireless communications, and movement capabilities, are
useful for tasks such as static sensor deployment, adaptive
sampling, network repair, and event detection [9]. These
advanced sensor network architectures could be used for a
variety of applications including intruder detection, border
monitoring, and military patrols. In these kinds of hostile or
potentially hostile environments, the security of unattended
mobile nodes is critical. The attacker may be able to capture
and compromise mobile nodes, and then he can use them to
inject fake data, disrupt network operations, and eavesdrop on
network communications.
PRELIMINARIES
In this section, we first present the problem statement and
underlying assumptions for our proposed scheme and then
describe the attacker models and design goals.
1) Problem Statement: In this work, we tackle the problem
of mobile replica node attacks. We define a mobile replica
node u0 as a node having the same ID and secret keying
materials as a mobile node u. An adversary creates replica
node u0 as follows: He first compromises node u and extracts
all secret keying materials from it. Then he prepares a new
node u0, sets the ID of u0 the same as u, and loads u’s secret
keying materials into u0.
MOBILE REPLICA DETECTION USING SEQUENTIAL PROBABILITY RATIO TEST
This section presents the details of our techniques to detect
replica attacks in mobile sensor networks.
In static sensor networks, a sensor node can be considered
to be replicated if it is placed at more than one location.
However, if nodes are allowed to freely roam throughout
the network, the above technique does not work because the
mobile node’s location will continuously change as it moves.
Hence, it is imperative to use some other technique to detect
replica nodes in mobile sensor networks. Fortunately, mobility
provides us with a clue that can help resolve the mobile replica
detection problem. Specifically, a mobile sensor node should
never move faster than the system-configured maximum speed.
CONCLUSIONS
We have proposed a replica detection scheme for mobile
sensor networks based on the Sequential Probability Ratio
Test (SPRT). We perform SPRT to detect mobile replicas by
using the basic idea that a mobile node should never move
at speeds in excess of the system-configured maximum speed.
Our scheme quickly detects mobile replicas with very small
number of location claims. Furthermore, we have presented
two types of attacks that might be launched by the attacker
and discussed the defense strategies against those attacks.
For future work, we would like to thoroughly explore how
localization and time synchronization errors affect the detection
accuracy of our scheme. We will examine analytically
and in simulation how time synchronization and localization
errors influence the false positive and negatives of the scheme.
Furthermore, we would like to evaluate our scheme against
various types of attacker models.
[attachment=26714]
INTRODUCTION
Advances in robotics have made it possible to develop a
variety of new architectures for autonomous wireless networks
of sensors. Mobile nodes, essentially small robots with sensing,
wireless communications, and movement capabilities, are
useful for tasks such as static sensor deployment, adaptive
sampling, network repair, and event detection [9]. These
advanced sensor network architectures could be used for a
variety of applications including intruder detection, border
monitoring, and military patrols. In these kinds of hostile or
potentially hostile environments, the security of unattended
mobile nodes is critical. The attacker may be able to capture
and compromise mobile nodes, and then he can use them to
inject fake data, disrupt network operations, and eavesdrop on
network communications.
PRELIMINARIES
In this section, we first present the problem statement and
underlying assumptions for our proposed scheme and then
describe the attacker models and design goals.
1) Problem Statement: In this work, we tackle the problem
of mobile replica node attacks. We define a mobile replica
node u0 as a node having the same ID and secret keying
materials as a mobile node u. An adversary creates replica
node u0 as follows: He first compromises node u and extracts
all secret keying materials from it. Then he prepares a new
node u0, sets the ID of u0 the same as u, and loads u’s secret
keying materials into u0.
MOBILE REPLICA DETECTION USING SEQUENTIAL PROBABILITY RATIO TEST
This section presents the details of our techniques to detect
replica attacks in mobile sensor networks.
In static sensor networks, a sensor node can be considered
to be replicated if it is placed at more than one location.
However, if nodes are allowed to freely roam throughout
the network, the above technique does not work because the
mobile node’s location will continuously change as it moves.
Hence, it is imperative to use some other technique to detect
replica nodes in mobile sensor networks. Fortunately, mobility
provides us with a clue that can help resolve the mobile replica
detection problem. Specifically, a mobile sensor node should
never move faster than the system-configured maximum speed.
CONCLUSIONS
We have proposed a replica detection scheme for mobile
sensor networks based on the Sequential Probability Ratio
Test (SPRT). We perform SPRT to detect mobile replicas by
using the basic idea that a mobile node should never move
at speeds in excess of the system-configured maximum speed.
Our scheme quickly detects mobile replicas with very small
number of location claims. Furthermore, we have presented
two types of attacks that might be launched by the attacker
and discussed the defense strategies against those attacks.
For future work, we would like to thoroughly explore how
localization and time synchronization errors affect the detection
accuracy of our scheme. We will examine analytically
and in simulation how time synchronization and localization
errors influence the false positive and negatives of the scheme.
Furthermore, we would like to evaluate our scheme against
various types of attacker models.