Integrated Security and Attack Detection Scheme for Wireless Sensor Networks

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International Journal of Modern Trends in Engineering
and Research
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e-ISSN: 2349-9745
p-ISSN: 2393-8161
Integrated Security and Attack Detection Scheme for
Wireless Sensor Networks
N. S. KAVITHA1
, P. SUGANTHI2
, Mr. R. Subramanian3
Assistant Professors, CSE,,
1,2
Erode Sengunthar Engineering College, Thudupathi, Palakarai, Tamil Nadu, India
Abstract- The wireless sensor node is a tiny device that is used to capture environment information.
Sensor devices are used to capture temperature and pressure details from the environment. The
sensor devices are used in hospitals, home and production plants. The main components of a sensor
node are microcontroller, transceiver, external memory and power source. A wireless sensor network
(WSN) is a wireless network consisting of spatially distributed autonomous devices. Sensors are
used to cooperatively monitor physical or environmental conditions. Sensor network is equipped
with a radio transceiver or other wireless communications device. The sensor networks are deployed
with consideration of sensing and transmission coverage factors.
Sensor network security protocols provide confidentiality for the messages. Object location and data
sink information are the sensitive elements in the sensor network. Two techniques are used to
provide location privacy to monitored objects. They are Source-location privacy and Sink-location
privacy. Periodic collection and Source simulation models are used in Source-location privacy
technique. Sink simulation and backbone flooding models are used in Sink-location privacy
technique. Communication cost and latency factors are consider in the privacy protection model.
Source and destination location details are protected in the privacy model.
The proposed system integrates the location privacy and data security process for the wireless sensor
network. Region based query model is used to improve location privacy. Confidentiality and
integrity techniques are used for the security process. Rivest Cipher (RC4) algorithm and Secure
Hashing Algorithms (SHA) are used for the data security.
I. INTRODUCTION
A Wireless Sensor Network (WSN) is a collection of spatially deployed wireless sensors by which to
monitor various changes of environmental conditions in a collaborative manner without relying on
any underlying infrastructure support. Recently, a number of research efforts have been made to
develop sensor hardware and network architectures in order to effectively deploy WSNs for a variety
of applications. Due to a wide diversity of WSN application requirements, however, a general-
purpose WSN design cannot fulfill the needs of all applications. Many network parameters such as
sensing range, transmission range, and node density have to be carefully considered at the network
design stage, according to specific applications. To achieve this, it is critical to capture the impacts of
network parameters on network performance with respect to application specifications.
Intrusion detection a WSN can be regarded as a monitoring system for detecting the intruder that is
invading the network domain. The intrusion detection application concerns how fast the intruder can
be detected by the WSN. If sensors are deployed with a high density so that the union of all sensing
ranges covers the entire network area, the intruder can be immediately detected once it approaches
the network area. However, such a high-density deployment policy increases the network investment
and may be even unaffordable for a large area. In fact, it is not necessary to deploy so many sensors

International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 02, Issue 01, [January - 2015] e-ISSN: 2349-9745, p-ISSN: 2393-8161
to cover the entire WSN area in many applications, since a network with small and scattered void
areas will also be able to detect a moving intruder within a certain intrusion distance. In this case, the
application can specify a required intrusion distance within which the intruder should be detected.
The intrusion distance is referred as D and defined as the distance between the point the intruder
enters the WSN, and the point the intruder is detected by the WSN system. This distance is of central
interest to a WSN used for intrusion detection.
In this system derived the expected intrusion distance and evaluate the detection probability in
different application scenarios. Given a maximal allowable intrusion distance Dmax = ε, we
theoretically capture the impact on the detection probability in terms of different network parameters,
including node density, sensing range, and transmission range. For example, given an expected
detection distance E(D), we can derive the node density with respect to sensors’ sensing range,
thereby knowing the total number of sensors required for WSN deployment.
In a WSN, there are two ways to detect an object: single-sensing detection and multiple-sensing
detection. In the single-sensing detection, the intruder can be successfully detected by a single
sensor. On the contrary, in the multiple-sensing detection, the intruder can only be detected by
multiple collaborating sensors. In some applications, the sensed information provided by a single
sensor might be inadequate for recognizing the intruder. It is because individual sensors can only
sense a portion of the intruder. For example, the location of an intruder can only be determined from
at least three sensors’ sensing data. In view of this, we analyze the intrusion detection problem under
two application scenarios: single-sensing detection and multiple-sensing detection.
II. RELATED WORKS
Location privacy has been an active area of research in recent years. In location-based services, a
user may want to retrieve location-based data without revealing her location. Techniques such as k-
anonymity [2] and private information retrieval [10] have been developed for this purpose. In
pervasive computing, users’ location privacy can be compromised by observing the wireless signals
from user devices [7]. Random delay and dummy traffic have been suggested to mitigate these
problems. Location privacy in sensor networks also falls under the general framework of location
privacy. The adversary monitors the wireless transmissions to infer locations of critical
infrastructure. There are some challenges unique to sensor networks. First, sensor nodes are usually
battery powered, which limits their functional lifetime. Second, a sensor network is often
significantly larger than the network in smart home or assisted living applications.
Source-location privacy: Prior work in protecting the location of monitored objects sought to
increase the safety period, i.e., the number of messages sent by the source before the object is located
by the attacker. The flooding technique has the source node send each packet through numerous
paths to a sink, making it difficult for an adversary to trace the source. Fake packet generation
creates fake sources whenever a sender notifies the sink that it has real data to send. The fake senders
are away from the real source and approximately at the same distance from the sink as the real
sender. Phantom single-path routing [4] achieves location privacy by making every packet walk
along a random path before being delivered to the sink. Cyclic entrapment creates looping paths at
various places in the network to fool the adversary into following these loops repeatedly and thereby
increase the safety period. However, all these techniques assume a local eavesdropper who is only
capable of eavesdropping on a small region. A global eavesdropper can easily defeat these schemes
by locating the first node initiating the communication with the base station.

Recently, several techniques have been proposed to deal with global eavesdroppers. Yang et al.
propose to use proxies to shape the network traffic such that global eavesdroppers cannot infer the
locations of monitored objects [9]. Shao et al. propose to reduce the latency of real events without
reducing the location privacy under a global eavesdropper. This technique ensures that the adversary
cannot determine the real traffic from statistical analysis.
Sink-location privacy: Deng et al. described a technique to protect the locations of sinks from a
local eavesdropper by hashing the ID field in the packet header. In [8], it was shown that an
adversary can track sinks by carrying out time correlation and rate monitoring attacks. To mitigate
these two kinds of attacks, Deng et al. introduced a multiple-parent routing scheme, a controlled
random walk scheme, a random fake path scheme, and a hot spots scheme. Redundant hops and fake
packets are added to provide privacy when data are sent to the sink. These techniques all assume that
the adversary is a local eavesdropper. A global eavesdropper can easily defeat these schemes. For
example, the global eavesdropper only needs to identify the region exhibiting a high number of
transmissions to locate the sink. We, thus, focus on privacy-preserving techniques designed to defend
against a global eavesdropper.
III. DATA COMMUNICATION IN WSN
A wireless sensor network (WSN) typically consists of a large number of small, multifunctional, and
resource-constrained sensors that are self-organized as an ad hoc network to monitor the physical
world [1]. Sensor networks are often used in applications where it is difficult or infeasible to set up
wired networks. Examples include wildlife habitat monitoring, security and military surveillance,
and target tracking. Location privacy is, thus, very important, especially in hostile environments.
Failure to protect such information can completely subvert the intended purposes of sensor network
applications. Location privacy measures, thus, need to be developed to prevent the adversary from
determining the physical locations of source sensors and sinks. Due to the limited energy lifetime of
battery-powered sensor nodes, these methods have to be energy efficient. Since communication in
sensor networks is much more expensive than computation, we use communication cost to measure
the energy consumption of our protocols.
Providing location privacy in a sensor network is challenging. First, an adversary can easily intercept
network traffic due to the use of a broadcast medium for routing packets. He can use information like
packet transmission time and frequency to perform traffic analysis and infer the locations of
monitored objects and data sinks. Second, sensors usually have limited processing speed and energy
supplies. It is very expensive to apply traditional anonymous communication techniques for hiding
the communication between sensor nodes and sinks. We need to find alternative means to provide
location privacy that accounts for the resource limitations of sensor nodes. In this paper, we focus on
privacy-preserving communication methods in the presence of a global eavesdropper who has a
complete view of the network traffic. The contributions in this paper are twofold.
 We point out that the assumption of a global eavesdropper who can monitor the entire
network traffic is often realistic for highly motivated adversaries. We then formalize the
location privacy issues under such an assumption and apply an analysis based on Steiner trees
to estimate the minimum communication cost required to achieve a given level of privacy.
 We provide the first formal study of how to quantitatively measure location privacy in sensor
networks. We then apply the results of this study to evaluate our proposed techniques for
location privacy in sensor networks. These include two techniques that hide the locations of
monitored objects—periodic collection and source simulation—and two techniques that

provide location privacy to data sinks—sink simulation and backbone flooding. Our analysis
and simulation studies show that these approaches are effective and efficient.
IV. PROBLEM STATEMENT
Sensor network security protocols provide confidentiality for the messages. Object location and data
sink information are the sensitive elements in the sensor network. Two techniques are used to
provide location privacy to monitored objects [15]. They are Source-location privacy and Sink-
location privacy. Periodic collection and Source simulation models are used in Source-location
privacy technique. Sink simulation and backbone flooding models are used in Sink-location privacy
technique. Communication cost and latency factors are consider in the privacy protection model.
Source and destination location details are protected in the privacy model. The following problems
are identified from the current security methods in WSN. Location dependent attacks are not
efficiently handled. Dynamic data update process is not managed. Communication overhead is high.
Data security is not provided.
V. PRIVACY EVALUATION MODEL
We describe a model for evaluating the location privacy of critical components in sensor networks.
In this model, the adversary deploys a snooping network to monitor the wireless transmission
activity in the target network. We consider a scenario in which an adversary can monitor all
transmissions of the sensors in the network. In practice, the adversary does not need to know exactly
where a packet is sent or the exact location of the sensor node that sends the packet. A rough
estimate of the location will be good enough for the attacker to conduct traffic analysis. However, we
assume the worst case scenario: for every observed packet, the adversary knows where it is sent or
which sensor node sends the packet. This indicates that each sensor i is an observation point, and a
tuple (i, t, e) is available to the adversary by observing each packet e send by node i at time t. We
assume that all transmissions are encrypted, and hence, the actual useful information available to the
adversary is (i, t). We assume that the network begins operations at time t = 0.
The attacker’s objective is to locate the source or the sink by snooping on the wireless transmissions.
The main observation used by the global adversary is: there must be a sequence of spatial-temporal
correlated packets involved in each communication from the source to the sink. As long as the
adversary knows the routing protocol, he can easily identify all these sequences from the traffic and
determine the set of possible sources and sinks. Intuitively, the defender has to create dummy
sequences in the network to confuse the attacker; such dummy sequences usually require the addition
of dummy traffic into the network, leading to more communication overhead. Clearly, there is a
trade-off between the location privacy and the communication overhead. We develop a theoretical
study of this trade-off.
We now describe our privacy model in detail. We will first describe a privacy model for source-
location privacy and then extend it to include sink-location privacy. Some notations frequently used
in this paper, their specific meanings will be further explained in our discussion below. A sensor
network deployed for an application can be viewed as a graph G = {V, E} where the set of vertices V
is the union of the set I of sensor nodes and the set of sinks. A small subset of the sensors will be the
source nodes. The set E of edges includes all direct communication links between sensor nodes
physically close to each other. At any point in time, from the global eavesdropper’s point of view,
the network can be considered to include a set SP, a set SA and a set of sensors that transfer data
between sources and sinks. The adversary eavesdrops on the entire network with the intention of
physically locating objects. We model each observation of the adversary as the tuple (i, t),

representing the fact that a packet has been emitted by a node i and observed by the adversary at time
t.
VI. PRIVACY-PRESERVING ROUTING
We present the proposed privacy-preserving techniques for protecting the location information of
monitored objects and data sinks. We assume that all communications between sensor nodes in the
network are encrypted so that the contents of packets appear random to the global eavesdropper.
Many key predistribution protocols can be used for this purpose [5]. We present two techniques to
provide location privacy to monitored objects in sensor networks, periodic collection and source
simulation. The periodic collection method achieves the optimal privacy but can only be applied to
applications that collect data at a low rate and do not have strict requirements on the data delivery
latency. The source simulation method provides practical trade-offs between privacy, communication
overhead, and latency.
6.1. Sink-Location Privacy Techniques
Two privacy-preserving routing techniques for sink-location privacy in sensor networks: sink
simulation and backbone flooding. The sink simulation method achieves location privacy by
simulating sinks at specified locations, and the backbone flooding method provides location privacy
by flooding the event reports in a backbone network that covers the data sinks. Both techniques
provide trade-offs between privacy, communication cost, and latency. In this work, we focus on
protection of passive sinks that only receive data from sensors. We will consider location privacy for
sinks that broadcast packets in future work.
In backbone flooding, we send packets to a connected portion of the network, the backbone, instead
of sending them directly to a few sinks. The packets are only flooded among the backbone members,
the sensors that belong to this backbone. As long as the real sinks are located in the communication
range of at least one backbone member, they can receive packets from any source in the field.
Clearly, for a global eavesdropper, the sink could be anywhere near the backbone. We assume that
the backbone is created soon after the network is deployed and that the adversary does not eavesdrop
until the backbone is created.
The main component of backbone flooding is the construction of the backbone. Existing studies have
focused on finding the minimal number of sensors that are needed to flood a packet so that the entire
network can receive it [11]. In our case, we need to flood the packets to cover an area large enough
to achieve the desired level of location privacy. In backbone flooding, we create a backbone
consisting of |L| members, such that each sink is within the range of at least one backbone member.
Given |L|, the backbone formed should cover as large an area as possible for maximum location
privacy. However, finding optimal solutions has been shown to be NP-hard [12].
Every new sensor v added to the set L will send an election message to find the number of uncovered
sensors each neighbor can cover. If maxv(m) outputs a valid sensor ID and the coverage of this node,
the ID of this node will be added to L. The newly added sensor will then execute the same algorithm.
If maxv(m) = , v will collaborate with existing nearby backbone members to find a usable sensor
using Algorithm 1. The backbone is a tree structure with backbone members as the tree nodes.
During the collaboration, the sensor will need to get information from its parent to find a node that
can cover at least m nodes. This collaboration could continue to next level of ancestors if such a node
is not known to the immediate parent. This backtracking process continues until a sensor meeting the
required constraints is found. If a sensor that can cover at least m uncovered sensors is unavailable, a
sensor that covers the maximum number of uncovered sensors is used.

Algorithm 1. Backtrack Procedure
1: procedure BACKTRACK(Coverage, Id,m)
2: ResultId Id
3: Max  Coverage
4: LocalMaxId -1
5: CollectCoverageInfo(GetMyId(),NULL)
6: (LocalMaxId,Max) MaxId(m)
7: if Max  m then
return LocalMaxId, Max
8: else if Max < Coverage then
9: ResultId = LocalMaxId
10: Max = Coverage
11: end if
12: for EachUnvisitedNeighborBKMember do
13: (Id,Coverage) = Backtrack(Max, ResultId,m)
14: if Coverage  m then
15: ResultId = Id
16: Max = Coverage
17: break
18: else if Coverage > Max then
19: Max = Coverage
20: ResultId = Id
21: end if
22: end for return ResultId,Max
23: end procedure
The beginning and termination of the backtracking process depends on the value of m. A value of m
≤ 1 would mean that backtracking would start only if v cannot find any neighbor that can cover at
least one uncovered sensor. If m has a value greater than the number of neighbors any sensor could
have, then the backtracking process would ensure that the sensor that covers the maximum number
of uncovered sensors is selected. Intuitively, this would mean that an increase of m would help in
covering more sensors with the help of fewer backbone members. More energy will be consumed to
form a backbone for a large m due to more backtracking steps.
VII. INTEGRATED SECURITY AND ATTACK DETECTION SCHEME
The proposed system integrates the location privacy and data security process for the wireless sensor
network. Region based query model is used to improve location privacy. Confidentiality and
integrity techniques are used for the security process. Rivest Cipher (RC4) algorithm and Secure
Hashing Algorithms (SHA) are used for the data security. The system is designed to provide location
privacy and data security for sensor networks. Adhoc query processing is performed in the system.
Location privacy is provided in node and sinks level. The system is divided into six major modules.
They are network deployment, data capture, query submission, sink level privacy, node level privacy
and security management. The network deployment module is designed to construct wireless sensor
networks. The data capture process module is designed to perform data sensing operations. Query
submission module is designed to handle query submission tasks. Sink level privacy is provided
under Sink privacy module. The node level privacy is provided under the node privacy module. The
security management module is designed to handle key and data distribution process.

7.1. Network Deployment
The network deployment module is designed to setup wireless sensor nodes. Sensor node and its
properties are collected from the user. Sensor nodes are placed with its coverage information. The
network is divided into a set of clusters.
7.2. Data Capture
The data capture process is performed in the sensor nodes. The data values are captured and updated
into the memory. The data values are updated with time information. The sensor nodes are managed
by the sink nodes.
7.3. Query Submission
The query submission process is initiated from the base station or external systems. The query values
are issued with node and time information. Node and sink level query values are used in the system.
Query values are prepared with node identification protection process.
7.4. Sink Level Privacy
The sink level privacy model protects the query processing under the sink nodes. The sink node
collects the query value from the base station. The sink node redirects the query value to the
downlink nodes. The query response is collected from the node by the sink node.
7.5. Node Level Privacy
Node level privacy model is used to protect sensor node based query values. The query values are
issued for the selected node. Query details are encrypted with node level key values. Node
identification values are protected by the system.
7.6. Security Management
The system provides security for query response from the sensor nodes. Rivest Cipher (RC4)
algorithm is used for the data confidentiality process. Data integrity is verified using the Secure
Hashing Algorithm (SHA). Key values are distributed from the base station.
VIII. CONCLUSION
Sensor networks are constructed to capture environment information. Data centers collect
information from sensor devices. Location privacy is provided for the sensor node and sinks node
levels. Data security is integrated with the location privacy scheme. The system manages the data
collection and analysis operations in sensor networks. The system reduces the bandwidth and battery
power consumption. Sensitive location and data values are protected by the system. Traffic level is
reduced by the system.
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Integrated Security and Attack Detection Scheme for Wireless Sensor Networks

Integrated Security and Attack Detection Scheme for Wireless Sensor Networks

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