EFFICIENT CLONE NODE DETECTION AND ELIMINATION IN WIRELESS SENSOR NETWORKS

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EFFICIENT CLONE NODE DETECTION AND

ELIMINATION IN WIRELESS SENSOR NETWORKS

V.Lincy Shobika PG research scholar Department of Computer Science

S.N.R Sons College,CBE-06 [email protected]

Dr.N.Sumathi Associate Professor Department of Computer Science

S.N.R Sons College,CBE-06 [email protected]

ABSTRACT

In the Wireless sensor networks are vulnerable to the node clone, and several protocols have been proposed to detect this attack. So sensor networks are required too strong assumptions to be practical for large-scale, deployed sensor networks. We use two novel node clone detection protocols with different tradeoffs on network conditions and performance. The first one is based on a distributed hash table (DHT) in which Chord algorithm is used to detect the cloned node, every node is assigned with the key, before transmits the data nodes has to give its key which would be verified by the witness node. If same key is given by another Node then the witness node identifies the duplicated Node. The second one is based on the Distributed Detection Protocol which is same as Distributed Hash Table (DHT), but it is easy and cheaper implementation. Every node only needs to know the neighbor-list containing all neighbor IDs and its locations. In the proposed work, we are implementing RDE protocol, by location based nodes identification, where every region/location will have a group leader. The leader will generate a random number with time stamp to the available nodes in that location. Witness nodes verify the random number and time stamp to detect the duplicated node. The user messages are also encrypted for security purpose.

Keywords

DHT, Cloned Nodes, adversary’s, Wireless Networks.

1. INTRODUCTION

A wireless sensor network is a collection of

nodes organized into a cooperative network. Each node

consists of processing capability may contain multiple

types of memory, have a Radio Frequency transceiver

have a power source, and accommodate various sensors

and actuators. Nodes communicate wirelessly and often

self-organize after being deployed in an ad hoc fashion.

Systems of 1000s nodes are anticipated; such systems

can revolutionize the way we live and work. Currently,

wireless sensor networks are beginning to be deployed

at an accelerated pace. It is not unreasonable to expect

that in 10-15 years that the world will be covered with

wireless sensor networks with access to them via the

Internet. This new technology is exciting with unlimited

potential for numerous application areas including

military, transportation, entertainment, crisis

management, environmental, medical, homeland

defense, and smart spaces.

Wireless sensor networks gained a great deal

of attention in the past decade due to their wide range of

application areas and formidable design challenges.

Wireless sensor networks consist of hundreds and

thousands of low-cost, resource-constrained, distributed

sensor nodes, which usually scatter in the surveillance

area randomly, working without attendance. The

operation environment is hostile, security mechanisms

against adversaries should be taken into consideration.

Among many physical attacks to sensor networks, the

duplicate node is a serious and dangerous one. Because

of production expense limitation, nodes are generally

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al secret credentials, and use those materials to clone

many nodes out of off-the-shelf sensor hardware. Those

duplicated nodes that seem legitimate can freely join the

sensor network and then significantly enlarge the

adversary’s capacities to manipulate the network

maliciously. For example, those victim nodes occupy

strategic positions and cooperatively corrupt the

collected information. A large number of duplicated

nodes under command, the adversary may even gain

control of the whole network. Furthermore, the node

duplication will exacerbate most of inside attacks

against sensor networks. In this paper, we present two

novel, practical duplication detection protocols with

different tradeoffs on network conditions and

performance.

2. RELATED WORKS

The basic challenge for any distributed

protocol for detecting node replicas is to minimize

communication and per node memory costs while

ensuring that the adversary cannot defeat the protocol.

A protocol that deterministically maps a node’s ID to a

unique witness node would minimize communication

costs and memory requirements per node, but would not

offer much security because the adversary would need

to compromise just a single witness node in order to be

able to introduce a replica without detection.

Unfortunately, sensor nodes typically employ

low-cost commodity hardware components unprotected

by the type of physical shielding that could preclude

access to a sensor’s memory, processing, sensing and

communication components. Cost considerations make

it impractical to use shielding that could detect pressure,

voltage, and temperature changes that an adversary

might use to access a sensor’s internal state. Deploying

unshielded sensor nodes in hostile environments

enables an adversary to capture, replicate, and insert

duplicated nodes at chosen network locations with little

effort. Thus, if the adversary compromises even a single

node, she can replicate it indefinitely, spreading her

influence throughout the network. If left undetected,

node replication leaves any network vulnerable to a

large class of insidious attacks. Using replicated nodes,

the adversary can subvert data aggregation protocols by

injecting false data or suppressing legitimate data.

Further, blame for abnormal behavior can now be

spread across the replicas, reducing the likelihood that

any one node exceeds the detection threshold. Even

more insidiously, node replicas placed at judiciously

chosen locations can revoke legitimate nodes and

disconnect the network by triggering correct execution

of node-revocation protocols that rely on threshold

voting schemes. Previous approaches for detecting node

replication typically rely on centralized monitoring,

since localized voting systems cannot detect distributed

replication.

A. Sensor Network Environments

A sensor network typically consists of

hundreds, or even thousands, of small, low-cost nodes

distributed over a wide area. The nodes are expected to

function in an unsupervised fashion even if new nodes

are added, or old nodes disappear. While some

networks include a central location for data collection,

many operate in an entirely distributed manner,

allowing the operators to retrieve aggregated data from

any of the nodes in the network. Furthermore, data

collection may only occur at irregular intervals. For

example, many military applications strive to avoid any

centralized and fixed points of failure. Instead, data is

collected by mobile units that access the sensor network

at unpredictable locations and utilize the first sensor

node they encounter as a conduit for the information

accumulated by the network. Since these networks often

operate in an unsupervised fashion for long periods of

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collection cycle, the adversary has time to use its

presence in the network to corrupt data, decommission

legitimate nodes, or otherwise subvert the network’s

intended purpose.

Thus far, protocols for detecting node replication

have relied on a trusted base station to provide global

detection. For the sake of completeness, we also discuss

the use of localized voting mechanisms. We consider

these protocols in the abstract; for specific examples of

previous protocols. Until now, it was generally believed

that these two alternatives exhausted the space of

possibilities. This paper expands the design space to

offer new alternatives with strong security and

efficiency characteristics.

B. Centralized Detection

The most straightforward detection scheme

requires each node to send a list of its neighbors and

their claimed locations to the base station. The base

station can then examine every neighbor list to look for

replicated nodes. If it discovers one or more replicas, it

can revoke the replicated nodes by flooding the network

with an authenticated revocation message.

C. Local Detection

To avoid relying on a central base station, we could

instead rely on a node’s neighbors to perform

replication detection. Using a voting mechanism, the

neighbors can reach a consensus on the legitimacy of a

given node. Unfortunately, while achieving detection in

a distributed fashion, this method fails to detect

distributed node replication in disjoint neighborhoods

within the network. As long as the replicated nodes are

at least two hops away from each other, a purely local

approach cannot succeed.

3. PROPOSED WORK

We use two novels in node clone detection and

elimination protocols with different tradeoffs on

network conditions and performance. The first one is

based on a distributed hash table (DHT) which is

implemented by Chord algorithm; every node is

assigned with the unique key before it transmits the data

it has to give its key which would be verified by the

witness node. If same key is given by another Node

then the witness node identifies the cloned Node. The

second one is based on the Distributed Detection

Protocol which is same as DHT, but it is easy and

cheaper implementation. Here every node only needs to

know the neighbor-list containing all neighbor IDs and

its locations. So that can detect node clone with high

security level and holds strong resistance against

adversary’s attacks.

The straightforward node-to-network

broadcasting is a quite practical way to distributively

detect the node clone, in which every node collects all

of its neighbor’s identities along with their locations

and broadcasts to the network. The main problem in this

approach is its extremely high communication

overhead.

 Key-based caching and checking system is

constructed to catch cloned nodes.

 To detect and eliminate the Clone Attacks in

Wireless Sensor Networks.

Cloning detection ensures that the number of

attack edges is independent of the number of Cloning

identities and is limited by the number of trust relation

pairs between malicious users and honest users. Cloning

detection observes that if malicious users create too

many Cloning identities.

 Trusted central authority

 Limited by the number of trust relation pairs

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Fig.1.Clone node detection

Fig.2.Clone node elimination

A. DHT-BASED DETECTION PROTOCOL

The principle of our first distributed detection

protocol is to make use of the DHT mechanism to form

a decentralized caching and checking system that can

effectively detect cloned nodes. Essentially, DHT

enables sensor nodes to distributively construct an

overlay network upon a physical sensor network and

provides an efficient key-based routing within the

overlay network. A message associated with a key will

be transmitted through the overlay network to reach a

destination node that is solely determined by the key;

the source node does not need to specify or know which

node a message’s destination is the DHT key-based

routing takes care of transportation details by the

message’s key. Messages with a same key will be

stored in one destination node. Those facts build the

foundation for our first detection protocol. Fig.1.Shows

detected clone node in WSNs. Fig.2 shows the

eliminated clone node in WSNs.

As a beginning of a round of DHT-based clone

detection, the initiator broadcasts the action message

including a random seed. Then, every observer

constructs a claiming message for each neighbor node,

which is referred to as an examinee of the observer and

the message, and sends the message with probability

independently. The introduction of the claiming

probability is intended to reduce the communication

overwork in case of a high-node-degree network. In the

protocol, a message’s DHT key that determines its

routing and destination is the hash value of

concatenation of the seed and the examinee ID. By

means of the DHT mechanism, a claiming message will

eventually be transmitted to a deterministic destination

node, which will cache the ID-location pair and check

for duplicate node detection, acting as an inspector. In

addition, some intermediate nodes also behave as

inspectors to improve resilience against the adversary in

an efficient way.

CHORD

There are several different types of DHT

proposals to implement the DHT protocol, such as

CAN, Chord, and Pastry. Chord is widely used, and we

choose Chord as a DHT implementation to demonstrate

our protocol. However, our protocol can easily migrate

to build upon Pastry and present similar security and

performance results.

The technical core of Chord is to form a

massive virtual ring in which every node is located at

one point, owning a segment of the periphery. To

achieve pseudo-randomness on output, a hash function

is used to map an arbitrary input into a -bit space, which

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for our protocol, a node’s Chord point’s coordinate is

the hash value of the node’s MAC address. Nodes

divide the ring into segments by their Chord points.

Likewise, the key of a record is the result of the hash

function. Every node is responsible for one segment

that ends at the node’s Chord point, and all records

whose keys fall into that segment will be transmitted to

and stored in that node.

CONCLUSION

Sensor nodes lack tamper-resistant hardware

and are subject to the node duplication attack. In this

proposed work we present two distributed detection

protocols: One is based on a distributed hash table,

which forms a Chord network and provides the

key-based routing, and checking facilities for duplicated

node detection, and the other uses probabilistic directed

technique to achieve efficient communication overhead

for satisfactory detection probability. The randomly

directed exploration presents outstanding

communication performance and minimal storage

consumption for dense sensor networks. Moreover, the

proposed approach, the probability of detecting node

duplication is much higher than that achieved in

previous distributed protocols. However this protocol

detects and eliminates the clone node efficiently than

the previous proposed protocols.

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