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Selective Forwarding Detection (SFD) in Wireless Sensor Networks

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Security is the critical subject in wireless sensor networks. Therefore, WSNs are susceptible to several types of security attacks. One reason to attack sensor networks is the limited capacity of sensor nodes. The security attacks could affect the most significant applications in WSNs area such as military surveillance, traffic monitor, and healthcare. Thus, there are different types of detection approaches against security attacks on the network layer in WSNs. Also, there are severe constraints on sensor nodes like reliability, energy efficiency, and scalability, which affect the security in WSNs. Since the sensor nodes have limited capabilities for most of these constraints, a selective forwarding attack is difficult to detect in the networks. Malicious nodes in the selective forwarding attack, work as normal nodes. However, it attempts to find the sensitive messages and drop them before sending the packet to other nodes. In order to keep this type of attacks away from networks, we propose a multi layers approach (SFD) that preserves the safely of data transmission between sensor nodes while detecting the selective forwarding attack. Furthermore, the approach includes reliability, energy efficiency, and scalability. Keywords— Wireless Sensor Networks (WSNs) and Selective Forwarding Attacks.
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Selective Forwarding Detection (SFD) in Wireless
Sensor Networks
Naser M. Alajmi
Computer Science and Engineering Department
University of Bridgeport
Bridgeport, CT, USA
nalajmi@my.bridgeport.edu
Khaled M. Elleithy
Computer Science and Engineering Department
University of Bridgeport
Bridgeport, CT, USA
elleithy@my.bridgeport.edu
AbstractSecurity is the critical subject in wireless sensor
networks. Therefore, WSNs are susceptible to several types of
security attacks. One reason to attack sensor networks is the
limited capacity of sensor nodes. The security attacks could affect
the most significant applications in WSNs area such as military
surveillance, traffic monitor, and healthcare. Thus, there are
different types of detection approaches against security attacks
on the network layer in WSNs. Also, there are severe constraints
on sensor nodes like reliability, energy efficiency, and scalability,
which affect the security in WSNs. Since the sensor nodes have
limited capabilities for most of these constraints, a selective
forwarding attack is difficult to detect in the networks. Malicious
nodes in the selective forwarding attack, work as normal nodes.
However, it attempts to find the sensitive messages and drop
them before sending the packet to other nodes. In order to keep
this type of attacks away from networks, we propose a multi
layers approach (SFD) that preserves the safely of data
transmission between sensor nodes while detecting the selective
forwarding attack. Furthermore, the approach includes
reliability, energy efficiency, and scalability.
KeywordsWireless Sensor Networks (WSNs) and Selective
Forwarding Attacks.
I. INTRODUCTION
Sensor networks gather data that is necessary to include in
smart networks environments. For example, these
environments include home, transportation system, military,
healthcare, and buildings. The study of Wireless Sensor
Network is an active topic in computer science and
engineering. WSNs have an impact on economics, and effect
industrial industry. It contains numerous sensors, in fact these
sensors communicate with a vast number of small nodes via
radio links. Sensor networks have a source and a base station.
WSNs manage thousands of sensor nodes. A sensor consists of
four basic units, sensing unit: processing, transceiver, and
power [1]. Currently many distributed sensor networks can be
deployed, and have a self-organizing ability. Within the
computation ability technique of WSNs mechanism’s
development, the technique must insure that sensor nodes are
not overloaded with too many complicated functions.
The security of wireless sensor networks has been
extensively investigated over the past few years. WSNs are
susceptible to many types of attacks because they serve as an
open network with the limited resources of nodes. Therefore,
the obstacles of securing a wireless sensor network are the
main disadvantage for all devices. The most conventional
threats to the security of wireless sensor networks include
eavesdropping, node compromised, interrupt, modify or inject
malicious packets, compromised privacy and denial of service
attacks [2]. Networks have different applications. Therefore,
applications comprise several levels of monitoring, tracking,
and controlling. A group of applications are employed for
specific purposes. In military applications, sensor nodes
include monitoring, battlefield surveillance, and object
tracking. The battlefield monitors utilized in military
operations have prompted the development of WSNs. In
medical applications, sensors assist in patient diagnosis and
monitoring. Here, most applications are deployed to monitor an
area and then react when a sensitive factor is recorded [3]. In
general, sensor networks have potential applications in various
industrial such as environmental monitoring, factory
instrumentation and inventory tracking.
II. SELECTIVE FORWARDING ATTACKS
A network layer in WSNs is subjected to many types of
attacks. Furthermore, a sensor node may acquire advantages of
multi-hop by simply refusing to route packets. Therefore, it
could be executed all the time with the net result. If a
neighboring node marks a route through the malicious node,
then it will be unable to modify messages [4]. There are
assortments of attacks targeting the network layer. The attacker
can attack the routing protocol by injecting the path between
the source and the base station.
In WSNs, there are two types of attacks: insider and
outsider attacks. One of the insider attacks is referred to as a
selective forwarding attack. In selective forwarding attack, the
adversaries are able to create routing loops that attract or repeal
network traffic. Also, they can extend or shorten source
routers, generate false messages, and attempt to drop the
significant messages. The selective forwarding attack is hard to
detect particularly, when compromised nodes drop packets
selectively. The drop packets come from one node or a set of
nodes. A malicious node refuses to forward the messages or
drop packets randomly. Thus, the base station would not get
the entire messages [5,6].
III. RELATED WORKS
Yu and Xiao [6] proposed an approach based on
lightweight security to detect a selective forwarding attack in
the environment of sensor networks. The approach utilized a
multi-hop acknowledgment to launch alarms by obtaining
responses from the nodes that are located in the middle of
paths. Authors assumed the approach could identify malicious
sensor nodes. The aim of the detection attack is to send an
alarm when a malicious node is discovered, which indicates a
selective forwarding attack. The authors noted that the
detection accuracy of their approach exceeds 95% with an error
rate of 15%. Yu and Xiao employed two detection processes in
the scheme: a downstream process (the direction on the way to
the base station) and an upstream process (the direction on the
way to the source node). In the upstream process, a report
packet is created and sent to the base station hop by hop when
nodes detect a malicious node. Therefore, the base station
would receive the alarm packet and forward multiple hops that
are produced by the node. An acknowledgement packet and an
alert packet will drain the energy during detection.
The identification of suspect nodes is reported via an
intermediate node. First, Xiao, Yu, and Gao [7] proposed a
checkpoint-based method. In this approach, a node is randomly
selected as the checkpoint to send an acknowledgement
message for detecting the adversary. It is a mechanism used to
identify suspect nodes in a selective forwarding attack. They
have attempted to improve the technique by detecting an
abnormal packet in sensor networks. They assumed that any
compromised nodes could not create alert packets with the aim
of maliciously prosecuting other nodes. After collecting
evidence to determine whether the node is a malicious node,
the source nodes determine the position of the suspect node
according to the location. However, it is no guarantee for
reliable transmission of messages even though the adversary is
positioned by acknowledgement.
Tran Hoang and Eui-Nam [8] proposed an approach against
selective forwarding attacks that consists of a lightweight
detection mechanism. The detection is a centralized cluster,
which utilized the two-hop neighborhood node information and
overhearing technique. It is dependent on the broadcast nature
of sensor communication and the high density of sensors. Each
sensor node is provided with a detection module that is
constructed on an application layer. Sensor node sets routing
rules and two-hop neighbor knowledge to generate an alert
packet. Hoang and Nam suggested that the two routing rules
make the monitoring system more suitable. Thus, the first rule
is to determine if the destination node forwards the packet
along the path to the sink. It generates an alert packet with the
malicious factor α to the sender/source node. The second rule
governs that the monitor node waits and detects the packet that
was already forwarded along the path to the sink. It verifies the
two-hop neighbor knowledge to assess whether the destination
node is on the right path to the sink. If not, it generates an alert
packet with the malicious factor β to the sender/source node.
The detection module is responsible for passively detecting
a selective forwarding attack in its neighboring sensor node.
The malicious counter is defined as the threshold of abnormal
activity in a sensor node, which could not skip. When the
malicious counter crossed the threshold X, it revoked the
malicious node from its neighbor list. The authors have
assumed that the neighboring node should be recognized. The
neighboring node must be secure and confidential in the
deployment time. The network has a static topology and uses
key management to prevent any outside attacks. The selection
of one type of network topology prevents the scheme from
working with other topologies.
Huijuan Deng et al, [9] proposed a scheme for secure data
transmission and detecting a selective forwarding attack. They
used watermark technology to detect malicious nodes. Prior to
employing a watermark technique, they used a trust value to
determine a source path for message forwarding. The trust
value involves weighting the credit of each sensor node. The
author notes an error rate of 10% and detection accuracy
greater than 95%. They assumed that the base station is always
trustworthy and cannot be comprised by the adversary, which
renders the scheme inappropriate for real wireless sensor
networks. Every node has a trust value. At the beginning of
network initializing, all nodes should have the same trust value.
Huijuan Deng et al. utilized the watermark technique to
calculate the packet loss. Data transmission begins when an
optimal routing path is confirmed. The base station creates a κ
bits binary sequence as the original watermark message.
Therefore, a watermark message is part of the packets. A base
station compares the extract watermark to the original
watermark to detect a selective forwarding attack. The
simulation results reveal a channel error rate of 10% and
detection accuracy greater than 95%.
Chanatip et al. [10] have proposed a lightweight scheme.
They referred to it as a traffic monitor-based selective
forwarding attack detection scheme. They used Extra Monitor
(EM) to eavesdrop and monitor all traffic when transferring
data between nodes. They also employed RSSI to detect a
sinkhole attack. The value of RSSI is that four EM nodes can
be arranged to establish the positions of all sensor nodes, of
which the base station position should be (0,0). Chanatip et al.
have assumed that the network is static when sensor nodes are
deployed; thus, any change in the type of topology will
immediately affect their approach. They assumed that the
attackers could capture and damage the nodes. Therefore, all
sensor nodes must protect or use tamper robust hardware.
These assumptions have caused the detection scheme to drain
the energy of the sensor nodes and contribute to the high cost.
Fig 1. Sensor nodes during selective forwarding attacks
IV. PROPOSED SYSTEM
In wireless sensor network, several nodes transfer sensor
readings to the base station to process data. Military bases
might find the importance of using sensor networks in order to
explore enemy forces. Sensor nodes have limited sensing and
computation. Also, nodes have communication ability. Sensor
readings collect data when it detects unusual activities of
enemy forces such as warplanes, and war tanks movement in
battlefields. Data will be sent to the base station through
routers. As shown in Figure 1, the attacker compromised the
nodes by attacking the networks. In military applications,
selective forwarding attacks destroy the transmission packets
between the source and base station, and sometimes between
the sensor nodes. Malicious nodes refuse to transfer an entire
packet. It drops the sensitive information and then forwards the
remaining packet. Furthermore, physical attacks frequently
occur in WSNs because it is easy for adversaries to execute
them.
Our approach finds a secure route during the data
transmission. In this part, we introduce our assumptions and
detection approach. Sensor networks are susceptible to several
types of attacks. The malicious node attempts to make some
obstacles occur during transferring packets with in the
networks. The following obstacles may occur: forward
message to another path, generate inaccurate route in the
network, and delay transfer of the packets between nodes.
Fig 2. Example of selective forwarding attack
The selective forwarding attack in Figure 2 may happen
between sensor nodes. Thus, node “A” transfers the packets to
node “B” and then node “B” stops forwarding the packets to
node “C”. As a result node “B” may forward packets to a
malicious node. Therefore, packets will not arrive to the base
station.
A. Assumptions
Wireless sensor networks are complicated. In order to
create a simple solution to detect the selective forwarding
attack, we have made some assumptions for the approach
detection within significant applications that are susceptible in
networks. These assumptions should be acceptable in the
sensor networks. First of all, we assume that secured
communication should be part of the networks. Second,
Malicious nodes should not drop any packets prior to the
launching of the selective forwarding attack. Third, we assume
that the adversary cannot compromise a sensor node during the
deployment. Finally, we assume that authentication broadcast
protocols were applied to each sensor node.
B. Selective Forwarding Detection (SFD) Approach
In wireless sensor networks, the rule-based intrusion
detection system (IDS) is one of the mechanisms for protection
against the security attacks. Rule-based IDS are known as
signature-based IDS. The network layer in WSNs is threatened
via some attacks such as a wormhole attack, a sinkhole attack
and other types of attacks. Our proposal focuses on the
selective forwarding attack. We design multi layer approach,
which includes three security layers depicted in Figure 3. The
first layer is data receiving. In this layer, the important
information is filtered and stored. The information includes
message fields that are useful to the rule processing. The
second layer is rule processing. In this section, rules must be
applied to the stored data. The message can be rejected or
refused. In addition, no rules will be applied to the message
since it fails. The third layer is detection. The detection
approach saves energy by using low memory and it takes not
much time. It chooses a secure route to transfer data between
the source and the base station. Furthermore, SFD approach is
reliable, energy efficient, and scalable. All these factors are
significant for the sensor nodes. Our approach assumes that the
detection accuracy is high, even though the radio condition is
poor.
Fig 3. Detection steps in rules based IDS-Redrawn [11]
C. Performance Evaluation
Our approach is estimated through the simulation. We have
pointed on malicious detection rate and energy consumption. In
the simulation, 200 sensor nodes are deployed in an area
network size 500 * 500 square meters. Hence, each node has a
35 meters transmission range and sensing range of node is 30
meters. Consequently, the communication overheads are
decreased.
Energy is an important factor. Figure 4 shows the
performance of our approach for the energy consumption. The
node cost is about 5J energy with 160 static nodes and 40
mobility nodes. As a result, we used different percentage
malicious detection 2%, 4%, 8%, and 16%. Thus, the total of
malicious nodes and energy consumption are appearing.
During the increasing malicious nodes drop packet, our
approach can achieve energy under the overflow of attack.
Therefore, it can be accomplished up to 40% malicious nodes.
In Figure 5, the graph shows the energy consumption. The
node cost about 5J energy with 200 static nodes and no
mobility nodes. It is more than 98% as long as the noise error is
2-4%, and the malicious nodes are under 12%. In fact, we used
different percentage malicious detection 2%, 4%, 8%, and
16%. Thus, the total of malicious nodes and energy
consumption are appearing. As a result, the detection rate of
the malicious nodes will be impacted. We observe that our
approach is more efficient when in fact the number of detection
nodes increased.
Fig 4. Energy consumption under malicious attacks in WSN
Fig 5. Energy consumption under malicious attacks in WSN
V. CONCLUSION
Security of WSNs has become increasingly concerning.
The use of wireless sensor networks is increasingly employed
in environmental, commercial, health and military applications.
Secure of packet and the transmission period is the
fundamental need in WSNs. Selective forwarding attack might
be a sever threats on the wireless networks. In this paper, we
present an approach that detection selective forwarding attacks
over the WSNs. The monitor sensor nodes detect selective
forwarding attacks using detector. Our approach is efficient to
detect the attacks. Also, the approach includes reliability,
energy efficiency, and scalability. Analysis and simulation
show that our approach is more effective when the numbers of
detection nodes are increased.
REFERENCES
[1] I. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wirelss sensor
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[3] David Martins, and Herve Guyennet, “Wireless Sensor Network Attacks
and Security Mechanisms: A Short Survey”, 2010 IEEE.
[4] J. P. Walters, et al., "Wireless sensor network security: A survey,"
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[5] Karlof, C. and Wagner, D., “Secure routing in wireless sensor networks:
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Wireless Sensor Networks”, In Parallel and Distributed Processing
Symposiun, 2007. ISSNIP 2006, 20th International, page 8 pp., 2006.
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[9] Huijuan Deng, Xingming Sun, Baowei Wang, Yuanfu Cao, “Selective
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[10] Chanatip Tumrongwittayapak and Ruttikorn Varakulsiripunth,
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Wireless Sensor Networks”, ICICS 2009.
[11] A. da Silva, M. Martins, B. Rocha, A. Loureiro, L. Ruiz, and H. Wong,
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Naser Alajmi
Mr. Naser Alajmi is pursuing towards
his Ph.D., Department of Computer
Science and engineering at the University
of Bridgeport, Bridgeport, CT. Naser’s
interests are in Wireless Sensor Network (WSN), Wireless
Sensor Network Security, and Network Security.
Khaled Elleithy
Dr. Elleithy is the Associate Vice
President of Graduate Studies and
Research at the University of Bridgeport.
He is a professor of Computer Science and
Engineering. He has research interests are
in the areas of wireless sensor networks,
mobile communications, network security, quantum
computing, and formal approaches for design and verification.
He has published more than three hundred research papers in
international journals and conferences in his areas of expertise.
Dr. Elleithy has more than 25 years of teaching experience. His
teaching evaluations are distinguished in all the universities he
joined. He supervised hundreds of senior projects, MS theses
and Ph.D. dissertations. He supervised several Ph.D. students.
He developed and introduced many new
undergraduate/graduate courses. He also developed new
teaching / research laboratories in his area of expertise.
Dr. Elleithy is the editor or co-editor for 12 books by
Springer. He is a member of technical program committees of
many international conferences as recognition of his research
qualifications. He served as a guest editor for several
International Journals. He was the chairman for the
International Conference on Industrial Electronics, Technology
& Automation, IETA 2001, 19-21 December 2001, Cairo
Egypt. Also, he is the General Chair of the 2005-2013
International Joint Conferences on Computer, Information, and
Systems Sciences, and Engineering virtual conferences.
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