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Prevention of Wormhole Attacks in Wireless Sensor Networks

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Security of deployed wireless sensor networks (WSNs) has become a crucial task as the deployment of WSN'S grows. This paper proposes a novel way and develops a mechanism to prevent wormhole attacks by an algorithm to manage a large number of nodes using node ID. Also, the proposal creates a load balancing feature for monitoring a hierarchical system for nodes and extra packet header on each node to make sure it is not compromised. Even though an attack is inevitable on a live node network using a simulator, this paper has shown how the network nodes can be rerouted to avoid the attacked nodes.
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Prevention of Wormhole Attacks in Wireless Sensor Networks
1Dema Aldhobaiban , 2Khaled Elleithy and 3Laiali Almazaydeh
1,2 Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
3 Department of Software Engineering, Al-Hussein Bin Talal University, Ma’an, Jordan
Daldhoba@my.bridgeport.edu , elleithy@bridgeport.edu, lalmazay@my.bridgeport.edu
Abstract- Security of deployed wireless sensor networks
(WSNs) has become a crucial task as the deployment of
WSN’S grows. This paper proposes a novel way and develops
a mechanism to prevent wormhole attacks by an algorithm to
manage a large number of nodes using node ID. Also, the
proposal creates a load balancing feature for monitoring a
hierarchical system for nodes and extra packet header on each
node to make sure it is not compromised. Even though an
attack is inevitable on a live node network using a simulator,
this paper has shown how the network nodes can be rerouted
to avoid the attacked nodes.
Keywords: wireless sensor network, security, routing attack,
wormhole attack, node ID.
I. INTRODUCTION
A sensor node is also called as mote. In a wireless
sensor network, a node is capable to perform the
processing, and gathering of the sensor information for
communication with all other connected nodes in the
network. Sensors are the hardware devices: they measure
and produce a proper response by a physical condition like
pressure or temperature. Sensors are monitored by using a
physical data parameter. Generally, a sensor is small in size
to consume low energy and volumetric densities that
operates adaptive environment.
Node is applicable in the mobile applications that are
transmitted by Application Programming Interface. In
wireless networks, sensors challenge several characteristics,
constraints, and distinguish the contemporary
communication. Sensor nodes are tightly constrained in the
terms of energy, power, and bandwidth storage capacity.
Security is the major problem in wireless sensor
networks. Wormhole attacks can make the network
vulnerable. A wormhole attack means a node illegitimately
claims multiple nodes. This attack threatens wireless sensor
networks in routing, voting system, fair resource allocation,
data aggregation, and misbehavior detection .The research
is carried out to prevent the wormhole attacks and improve
the network performance The node ID-based scheme is
proposed, where the detection is based on node registration,
consisting of two phases and the assignment of ID to the
node is done dynamically. The ID's corresponding to the
nodes registered is at the base station, and the node active
time is monitored. Any abnormalities in the above phases
confirm the presence
of damaged and attacked nodes in the network. The
algorithm will be simulated using C#. The energy
consumed for this algorithm will also be calculated. The
proposed detection algorithm is analyzed based on the
network's Physicians' Desk Reference (PDR) and found
that the throughput has improved, which prove that this
algorithm may be used in the environment where security is
needed.
II. RELATED WORK
Advancement in Wireless Sensor Networks (WSNs)
and Micro-Electro Mechanical Systems (MEMS) have
continued to impact daily life from performance of complex
tasks such as monitoring life signs in hospitals to simple
tasks such as monitoring temperature. In this new field of
design, WSNs provide many applications such as military
applications and creation of context aware homes. An
example of such an application is Smart Dust. This new
design on the other hand faces many challenges which for a
long time were not considers feasible until the recently.
Creating an organizational structure is one of the problems
of the design. Researchers have come up with different
ways of creating the organizational structures (clusters)
because the WSNs have not been feasible in organizing the
nodes. Clustering is a significant phenomenon in the
organizational structure. Clustering forms an essential part
in the WSNs in the organizational of the network that
constituted of sensory nodes, clusters, cluster heads, base
stations, and end users.
Clustering considers the limitation of WSNs such as
limited energy, network lifetime, limited liabilities and
application dependency in affecting the performance of the
network. Implementation of WSNs poses great challenges
because the traditional designs have little basis. Clustering
algorithm is very essential in classified heuristic, weighted,
hierarchical, and grid schemes. There are many heuristic
algorithms that are used in choosing cluster heads such as
linked cluster algorithm(LCA), linked cluster algorithm
2(LCA2),highest connectivity cluster, and max-min D
cluster algorithm. In this d-hop max –Min provides the
overall characteristic as compared to the other heuristic
algorithms. This is because it produces a smaller number of
cluster heads, larger clusters and longer duration of cluster
heads as compared to LCA. In weighed Schemes, there are
weighed clustering algorithms (WCA), clustered election
procedure, and complexity due to distributiveness.
Hierarchical schemes include low-energy adaptive
clustering hierarchy (LEACH), two level hierarchy LEACH
(TL-LEACH), and energy efficient clustering scheme
(EECS), and hybrid energy efficient distributed clustering
(HEED).
For location and time based approaches, Hu, Perrig,
and Johnson [1] demonstrated a method that relied on
Packet Leashes, where geographic leash and temporal leash
are placed on upper bound location of the receiver and
maximum possible time frame needed for journey of
packet, and the temporal lash is dependent on TIK protocol
and ideas of geographical and time management are
required.
Taheri, Naderi, and Barekatain [2] utilized leashes
process with an enhanced packet distributor system to
reduce measurement costs of TESLA with Instant Key
Disclosure (TIK) protocol. In case of (TTM), Tran, Hung,
and Lee brothers [3] came up with a method where every
node is in place of path notes during transmitting RREQ
packet while collecting RREP packet and time frame is also
important. Singh and Vaisla [4] made an improvement by
switching sender and receptor against manipulating
response and proposal packet time rate. Hu and Evans [5]
provided a method where the locating antenna can check
details of neighbors by analyzing locations of HELLO
messages and detecting neighbors by verifiers.
Interestingly, this system can identify a threat from a spy by
coming up with definite pair of confidential keys, but help
from hardware is a must and wormholes with unreal
neighbors can be identified with this process.
For locating wormhole threats, Khalil, Bagchi, and
Shroff [6] had lightweight countermeasure (LITEWORP)
with security nodes and subsequently wormhole,
LITEWORP goes out of signal from that unlocked node
only and increases chances of blockades to solve this a
protocol called MOBIWORP [7], which had the ability to
get rid of threatening nodes by central power from regional
or international. Chen, Lou, and Wang [8] came up with a
protected method for identifying simplex and duplex
wormhole threats with a longer algorithm [8] for upgrading
it to tackle non-identical distribution length of sensory
nodes. Unfortunately, more than one wormhole can’t be
identified this way. Nait-Abdesselam, Bensaou, and Taleb
[9] elaborated on identification and evasion system based
on load support with diverse courses, and because of
crowded packets, it has a hefty load and might give wrong
alerts. Khurana and Gupta [10] , [11] suggested method was
about total span and utmost scope of nodes SEEEP [10] but
had drawbacks to nodes with similar distribution span
which was elongated as FEEPVR [11] to help non-identical
range or span.
Hayajneh, Krishnamurthy, and Tipper [12] proposed
SECUre NeighborhooD (SECUND) protocol for detecting
more than one wormhole without required ideas about node
locality, and clock management and does not need unique
hardware equipment but only performs when wormhole
augment unreal neighbors by a bigger number. Dimitriou
and Giannetsos [13] had an algorithm based on linkage data
and identified wormholes and works on locality routes
present assay when it finds up to date nodes. Gupta, Kar,
and Dharmaraja [14] submitted a method which can
identify wormholes by calculating jumps or hop distinctions
among neighbors that are nodes of single jump or hop away
and had unique hand packets which detain analysis. Vani
and Rao [15] suggested Wormhole-Evasion Route Reply
decision packet (WARRDP) which exposes wormholes and
ejection by fused process of hop calculation anomaly based
and neighbor list processes.
Wang and Bhargava [16] proposed Multi-Dimensional
Scaling- Visualization of Wormhole (MDS-VOW) with
central manipulator and is a centralized system without aid
of hardware equipment but works less for sparse network or
linkage. P. Radha, L. Loukas [17] proposed a graph method
which gives out probable features for locating and
protection from wormhole threat with a unique guard node
a longer radio span. Choi, Kim, Lee, and Jung [18] have
come up with a Wormhole Attack Prevention (WAP)
algorithm Dynamic Source Routing (DSR) protocol which
functions perfectly for secret threats, but for present threats,
it is not competent. Azer, Kassas and Soudani [19] had
advanced a well-functioning locator and evade method on
Diffusion of Innovations, but carriage time of both ends is
augmented. Finally, Poornima, Bindu and Munwar [20]
implemented a technique on geographic location with
reverse.
III. PROPOSED APPROACH
An approach for the wormhole prevention is presented
in this section with each node having a unique id linked to
the surrounding nodes in a binary tree structure. To get the
best optimal results of the experiment, derive a clustering
based approach were in the affected node id, and its
neighboring nodes links are severed from the rest of the
node structure. Figure 1 shows the links with the red nodes
effected and the purple links show the neighboring nodes.
The green circular node is the source node that was
infiltrated for the attack. In this approach we severed the
nodes connected to the source node as well as the
consecutive neighboring nodes. In the following case node
{1, 3, 7, and 9} are also severed from the rest of the
network to keep the infected nodes at bay.
Routing Scheme (RRS) also Authentication of Nodes
Scheme (ANS), for identifying wormholes operates for
BSR protocol, but the witness capacity is too serious. Attir,
Abdesselam, Brahim, Bensaou, and Ben-Othman [21]
presented a method by neighborhood identification by W-
Delay with extra data to HELLO packet for exposing
wormhole but is restricted to OLSR protocol with its
neighboring nodes.
Figure 1: Neighboring Node Structure under Attack
Figure 2: Flowchart depicting the proposed algorithm
Figure 2 shows the algorithmic approach, where each
acknowledgement has a packet analysis done for the
incoming data packets. The analyzed packets determine if
any affected nodes in the mesh network. Since each node
has a node id, it is easy to locate the infected node. A
neighboring count is done on the infected node and all the
links to and from the neighboring nodes are severed from
rest of the mesh network. The mesh network data table is
updated and the same process is run again until the whole
mesh network is clear of all the infected nodes.
IV. SIMULATION AND RESULTS
The experiment developed a simulation using c#
programming in windows form to simulate only the binary
structure of how the nodes are severed in a mesh network
and the source node connected along with the node ids. The
process inputs a basic building block to display the node
structure. Figure 3 depicts the simulation. The process can
input the number of nodes and the neighboring nodes for
each node in the network and simulate the results. The
output displayed is just for ten nodes with six neighboring
nodes. Since the increase in the number nodes makes the
view unclear, this is just for the display purpose. The
algorithm can run simulations of up to 100 nodes at a time
with 8 neighboring nodes that in a total of up to 1000
nodes.
Figure 3: Simulation for Wormhole attack
The left window depicts the binary structure as to how
the nodes are linked to each other using their node ids in the
mesh network. The binary structure indicates which node
subsets were infected in the mesh network. The right
window depicts infected node in the mesh network. The
green node is the source infected node, and the red lines
leading outwards from the source node show the links that
are affecting the neighboring nodes. The purple links depict
the secure and unaffected links. The black nodes are the
ones that will also be severed from rest of the network as
they are in immediate vicinity of the infected node. This
way eliminate a small number of nodes in and around the
infected node in a mesh network makes the whole mesh
network secure.
Figure 4 shows that the new network throughput
increased every time an infected node is found. The
infected node is severed from the network, and the data
table of the mesh network is updated and same throughput
of nodes are run up until the infected nodes are found. The
following is the result behind the increase in the throughput
during the time for simulating a packet that was already
severed the infected node from the network. The power will
turn off then and will turn on the new mesh network, and
attack nodes were turned off for shorter during this time.
Figure 4: Graphical Result
V. CONCLUSIONS
This research paper has shown a novel way of securing
a mesh network from wormhole attacks. The deletion of the
links for the infected node and its neighbor leads to the
security of the rest of the node network. Since this approach
requires a table to monitor all the nodes, the load on the
network is overwhelming. Thus to improve this, further
experiments are planned to use more than one routing table
in a very large mesh network. It will be similar to how
routers help in routing algorithms by keeping information
of its data sets in and around its vicinity.
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Chapter
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