IRJET- Robustness Strategy for Securing Data from Cyber Attacks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1322
Robustness Strategy for Securing Data from Cyber Attacks
Tanay P. Vartak1, Harshada V. Ankam2, Komal K. Randive3, Amrita A. Shirode4
1,2,3Student, Dept. of Computer Engineering, AISSMS Polytechnic, Maharashtra, India.
4Professor, Dept. of Computer Engineering, AISSMS Polytechnic, Maharashtra, India.
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Abstract - This paper first presents a new modeling
strategy to generate scale-free network topologies, which
considers the constraints in WSNs, such as thecommunication
range and the threshold on the maximum node degree. ROSE,
a robustness increasing algorithm for scale free wireless
sensor network, is proposed. Given a scale-freetopology, ROSE
utilize the position and degree information of nodes to
rearrange the edges to similar to an onion-like structure,
which has been proven to be robust against malicious attacks.
Meanwhile, ROSE does not change the degree of node such
that the resulting topology remains scale-free. The extensive
experimental results verify that our new modeling strategy
indeed generates scale-free network topologies for WSNs, and
ROSE can significantly improve the robustness of the network
topologies generated by our modeling strategy.
Key Words: Wireless sensor networks (WSNs), Node,
Layers, Degree, and Bandwidth
1. INTRODUCTION
Wireless sensor networks(WSNs)areanimportanttype
of network for sensing the environment and collecting
information. In order to sense environmental parameters, a
large number of sensor nodes, such as sink nodesandsensor
nodes, are deployed in distributed areas.Thesenodesforma
multi-hop ad hocnetwork systemand executeassignedtasks
according to the application requirements. WSNs have been
deployed in homes, buildings, forests, mountains, etc. The
sensor network topology describes the wireless
communications among the various sensor nodes in WSNs
and is the basis for the design of various network
communication protocolsandroutingprotocolswhichplaya
vital role in network properties, such as network lifetime,
energy consumption, reliability, and data latency.Inrandom
attacks, the attacker randomly choosesnodesinthenetwork
topology as the targets, whereas in malicious attacks, the
attacker chooses the nodes with high node degrees as the
targets. It is known that some types of network topologies
are resistant to random attacks and some are resistant to
malicious attacks. In this project, the proposed systemROSE
can significantly improve the robustness of the network
topologies generated by our modeling strategy. The scale-
free topology belongs to the field of complexnetwork theory
which has broad applications in the real world, such as in
global transportation networks cooperation networks of
social networks and mobile networks. The main contribute
in this paper malicious detected by using distributed
network.
1.1 ROSE Overview
ROSE is designed to be processed in a centralized
system. Before ROSE operates, each node sends its own
coordinates and neighbor list to the centralized system
through the multi-hop system. After we achieve the
optimization results according to ROSE, the centralized
system sends the new neighbor list to each nodethroughthe
multi-hop system. To help explain ROSE, we first introduce
the basic ideas of ROSE in this section. Then, we provide the
details of ROSE in the following section. In general, the
design of ROSE is based on the observation of Schneider et
al. that graphs exhibiting an onion-like structure are robust
to malicious attacks. Schneider etal.describedtheonion-like
structure as a structure “consisting of a core of highly
connected nodeshierarchicallysurroundedbyringsofnodes
with decreasing degrees.”NotethatSchneider etal. validated
their observation only by extensive simulations. One year
later, the theoretical analysis supporting this observation
was provided by Tanizawaet al. Since the onion-like
structure encompasses a family of network topologies,
Tanizawa et al. analyzed one specific topology called the
“interconnected random regular graphs” of this family, and
proved its robustness against malicious attacks. Given that
the above observation about the onion-like structure has
been validated both experimentally and theoretically, the
ROSE algorithm is aimed to transform network topologiesto
exhibit the onion-like structure. Specifically, ROSE involves
two phases: a degree difference operation and angle sum
operation.
2. Metrics of Robustness:
Under a local node failure or attacks, some nodes or
edges of the topology are destroyed in WSNs, which leads in
general to separation of the initial connected network. We
assume the WSNs suffer random and malicious attacks.
Random attacks comprise random selection and removal of
nodes in a WSN to destroy the connectivity of the entire
network. However, malicious attacks are aimed to destroy
the most important node in a WSN and achieve the worst
damage to the entire network topology. We determine the
importance of nodes according to the degree. A node with a
higher degree is more important than that with a lower
degree. We determine a malicious attack scheme that is
based on node degree to enhance the robustness of scale-
free networks against malicious attacks. First, the degree of
each node in the scale-free network is counted and the node
with highest degree is removed. The edges connected to it

are removed at the same time. Then, we reorder the
remaining nodes by their degree.Thecurrenthighestdegree
node is removed. This process is repeated until all the nodes
are isolated in the network. If more than one node has the
highest degree, we randomly select a node to attack. This
attack scheme is more hostile, and therefore, the metrics of
robustness based on this attack scheme can reflect the
robustness against malicious attacks “more obvious.”
Fig -1: Scale free network
Ref: ROSE: Robustness Strategy for Scale-Free
Wireless Sensor Networks
In a recent paper, a new metric of robustness based on
the percolation theory was proposed by Schneider et al. It
considers the maximal connected sub graphs after the
repeated removal of the highest degree node to measurethe
robustness of the network topology. We combined this
metric with the malicious attacks scheme presented above,
and describe network connectivity through calculating the
proportion of maximal connected sub graphs of the entire
network, and evaluate the extent of the damage. In a
network with N nodes, the robustness metric R isdefined as,
R=1N+1∑n=0N−1MCSnN (3) in our topology construction
strategy for WSNs, we restrict the order in which each node
adds edges. For this reason, the nodes near the center of the
network tend to have larger degrees than those near the
boundary of the network. If the distance of two nodes from
the coordinates of the centroid is the same, they have the
greatest probability of having the same or similar degrees.
3. Methodology
Due to the recent proliferation of cyber-attacks,
improving the robustness of wireless sensor networks
(WSNs), so that they can withstandnodefailureshasbecome
a critical issue. Scale-free WSNs are important, because they
tolerate random attacks very well; however, they are not
protected from malicious attacks, which particularly target
certain important nodes. In recent years, becauseoftherush
in cyber-attacks increasing the robustness of the WSNs has
become a critical issue. In existing a hill climbing algorithm
introduced which is based on robustness metric R, which
makes the network topologies resemble a stable distributed
network structure through swapping edges. However, the
multimodal phenomenon may prevent the algorithm from
jumping out of the local optimum.
The proposed methodology ROSE enhances the
robustness of scale-free networks against malicious attacks
without changing the node degree distribution. ROSE
consists of two phases: the degree difference and the angle
sum operation. Both operations are aimed to transform the
network topology toward the distributednetwork structure,
which was shown in to be robust against the malicious
attacks. The degree is assigned to each node to form the
ROSE structure. The degree is just like weight or BW or
capacity or range of node which is assigned by the system
randomly at the time of registration or network topology
creation.
Fig -2: System Architecture
In this higher degree node is cover up by the lower degree
nodes. In an distributed network structure, the nodes that
have the same or similar degrees are connected with each
other. All the neighbors of a high degree node have high
degrees. The main contributions in this paper malicious are
detected using distributed network.
3.1 Degree Difference operation
The degree is assigned to each node to form the ROSE
structure (distributed network –like structure). The degree
is just like weight or BW or capacity or range of node which
is assigned by the system randomly at the time of
registration or network topology creation. In this higher
degree node is cover up by the lower degree nodes. In a
distributed network -like structure, the nodes that have the
same or similar degrees are connected with each other. All
the neighbors of a high degree node have high degrees.
When the node fails, its neighbors can replace its original
function and ensure theconnectivityoftheresidual network.
Thus, the destruction of malicious attacks is weakened to a
great extent in WSNs. At the same time, the distributed

network -like structure retains the propertyofthescale-free
network. The majority of nodeshavelowdegrees.Theadmin
will send the signature or witness message to each node at
time of registration.
Depends on this signature or witness messages the
malicious nodes aredetected whichareaffected bymalicious
or random attacks. When attacker attacks the node the
signature / witness message will automatically changes.
These signatures are stored at admin side in form of digest
by using MD5 algorithm.
3.2 Distributed Network
The admin will send request to all nodes for witness
message to detect the malicious node in the network. Upon
receiving the request from the admin the node will send the
signature to admin. (The victim i.e. the infected nodedoesn’t
know about the attack). Once admin receives the signature
the system generates the digest of that message and
validation of digest message which is available at admin and
received digest is done. If doesn’t matches the system will
gives the alert for malicious node in the network.
4. CONCLUSION
A newly proposed algorithm called ROSE was designed
for enhancing the robustness of scale-free networks against
malicious attacks. The combination of a degree difference
operation and an angle sum operation in the algorithm
makes scale-free network topologies rapidly approach an
onion-like structure without changing the original power-
law distribution. Finally, the performance of ROSE was
evaluated on scale-free network topologies having different
sizes and edge densities. The simulation results show that
ROSE significantly improves robustness against malicious
attacks and retains the original scale-free property in WSNs
at the same time. ROSE shows better robustness
enhancement results and consumes less computation time.
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