A survey on bio inspired security in wireless sensor networks

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 05 | May-2015, Available @ http://www.ijret.org 449
A SURVEY ON BIO-INSPIRED SECURITY IN WIRELESS SENSOR
NETWORKS
S R Mani Sekhar1
, Abhijith S2
, Bellamkonda Maruthi3
, Bharath Kumar4
, Chetan Janiwarad5
1
M S Ramiah Institute of Technology, Bangalore
2
3
4
5
Abstract
Wireless sensor networks usually comprise of a large number of nodes which are geographically distributed and are not
physically connected. These nodes are frequently used to sense private data and can be necessary to transmit confidential and
critical data. Hence it is important to provide security for wireless sensor networks. Research is still ongoing in this field and
many models have been proposed for providing security. Looking into the symbiotic nature of biological systems can give us
valuable in- sights for computer networks. Because of the analogies between network security and how the biotic components
react to perceived threats in their surroundings, Bio-inspired approaches for providing security in networks are interesting to
evaluate . Many theories from nature such as swarm intelligence, ant colony optimisation (ACO), web spider defence, bird
flocking, human immune system and so forth have been used to tackle various problems in the networking domain. In this paper,
we intend to outline and categorize the various security attacks we encounter in a wireless sensor network and review the
proposed conventional security mechanisms for them and also compare it with an alternative novel approach, i.e bio-inspired
approach.
Keywords— Wireless sensor network (WSN), Bio-inspired, security, attacks
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1. INTRODUCTION
Wireless sensor networks are gaining significance in the
mod- ern day world because of their wide range of potential
applications in the fields of science, industry,
transportation, civil infrastructure, and military.
Communication over wireless medium is, by nature not
secure and is vulnerable to various threats and attacks. Due
to deployment of the nodes in physically hostile and harsh
environments, multi hop and distributed architecture, WSN
is more susceptible to different types of security attacks
and threats. It is easy for an attacker to launch security
attacks against physical, me- dia access, or network layer in
the WSN. Therefore some sort of security mechanism is
highly advisable. Establishing any efficient security scheme
in wireless sensor networks is made challenging by the
sensors size, processing or computing power of each
sensor node, memory and type of tasks expected to be
performed by the sensors.
In networks, security is a broad term that comprises of
varied parameters like authentication, integrity, privacy, and
non-repudiation [17]. A security framework in order to be
agreed upon, should not violate these requirements. Al-
though research in the field of sensor networks security is
progressing positively, it still lacks a comprehensive inte-
grated framework which can provide security to each layer
and services of sensor networks. Inspired by the implicit al-
luring characteristics of biological systems, many
researchers are working to produce new novel design
paradigms to address challenges in current network
systems[14].
Fig 1: Nodes in WSN[7]
Adaptabil- ity, scalability, robustness are a few of many
advantages that a bio-inspired approach provides. Biological
techniques usually are results of efforts of generations for
their struggle to survive harsh conditions. Bio-inspired
algorithms are built on simple rules and are usually not
complex. Attacks in WSNs can be analysed from two
different views. Attacks against the mechanisms which
provide security (security attacks) to the network and
attacks against basic mechanisms like routing and other
physical attacks. The different types of attacks that a
network can come up against are jamming, tampering, node

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capture, dos attack (physical layer), collision (link layer),
Sybil attack, wormhole, sinkhole attack, selective
forwarding (network layer) and flooding (transport layer).
These attacks can be negated by using security mech-
anisms like effective key management, cryptography, secure
broadcasting and multicasting, Intrusion detection System
(IDS). The biological algorithms: ant colony optimization
(ACO) is used for secure routing, spider defence
mechanism, human immune system [22] are used as
templates to react to the security attacks. The rest of the
paper is arranged as follows. Summary of the different types
of attacks in a wireless sensor network is given in section
2. Section 3 discusses the bio inspired algorithms studied so
far. In continuation to this, in Section 5 a comparison is
made between conventional and bio-inspired approaches to
securing wsns against attacks. Conclusion is given in the last
section.
2. TYPES OF ATTACKS IN WSN
Here, we look at some of the attacks (discussed in the ref-
erences) that tend to disrupt communications over wireless
networks, and categorize these attacks based on their effects
on data integrity and confidentiality, routing, identity, power
consumption, privacy, and service availability and
bandwidth related attacks[15].
2.1 Data Integrity and Confidentiality
2.1.1 Node Capture Attack
In Node Capture Attack, the attacker captures the sensor
nodes physically and thus those nodes are compromised and
the data accumulated in the nodes can be manipulated.
2.1.2 Eavesdropping Attack
In Eavesdropping attack or network sniffing, as the name
suggests information is retrieved from a network by
snooping on data being transmitted. The attacker is
clandestinely able to overhear a private conversation in an
illegitimate way.
2.1.3 Denial of Service (DoS) Attack
This attack is an attempt to make a network unavailable for
its authorized users. This type attack is implemented by
consuming the networks resources such as power supply,
memory so that it can no longer provide its intended service.
A DoS attack generally targets physical layer applications in
an environment where sensor nodes are located. Jamming,
Flooding and selective forwarding attacks discussed later are
variations of DoS attack.
2.2 Power Consumption Related Attacks
2.2.1 Sleep Deprivation Torture Attack
This type of attack targets the link layer. The attackers target
is to minimize the lifetime of the sensor nodes by in-
creasing power consumption. This can be implemented by
keeping the sensor nodes busy at all times depriving it of
any sleep time or rest.
2.2.2 Collision Attack
In collision attack, the attacker tries to manipulate the octet
configuration of transmitted packets simulating a collision.
When this happens then, the packets will be discarded due to
checksum mismatch.
2.3 Service Availability and Bandwidth
Consumption Related Attacks
2.3.1 Flooding Attack
In this type of attack, the attacker normally sends a sub-
stantial number of packets to the target node or to an access
point to prevent it from establishing or continuing a commu-
nication path.
2.3.2 Jamming Attack
This is a standard attack on a wireless sensor network,
where a node or set of nodes are simply jammed.
2.3.3 Selective Forwarding Attack
This attack is also known as Gray Hole attack. Here,
selected packets are dropped by a forwarding node other
unrelated or obsolete packets are forwarded instead. The
fraudulent node might also forward the message to the
wrong path, creating inconsistent routing information in the
network.
2.4 Routing Related Attacks
2.4.1 Wormhole Attack
In a wormhole attack or tunnel attack, an intruder manip-
ulates packets at one point in the network, tunnels them to
another point in the network, and then replays them into the
network[26]. An attacker infringes communications from
the sender, changes a portion or a whole packet, and speeds
up sending the changed packet through a specific wormhole
tunnel in such a way that the altered packet arrives at the
des- tination before the original packet which traverses
through the usual routes.
2.4.2 Sinkhole Attack
The sinkhole attack is a severe attack that prevents the base
station from obtaining complete and correct data, thus forms
a serious threat to higher-layer applications. In a Sinkhole
attack, a compromised node tries to draw all or as much
traffic as possible from a particular area.
2.4.3 Hello Flood Attack
The routing paths are burdened (flooded) with hello or ACK
messages
2.5 Identity Related Attacks

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2.5.1 Impersonation Attack
An attacker impersonates another nodes identity copying the
nodes MAC or IP address to enter the network or to launch
other attacks on the node.
2.5.2 Sybil Attack
This is a duplication attack in which a single node provides
multiple images of itself in the network when attacked. A
single node presents itself to other nodes with multiple
spoofed identifications (either MAC or network addresses).
3. BIO-INSPIRED ALGORITHM
Researchers over the past decade have explored nature to
find a comprehensive solution to the challenges faced in the
field of wireless networks. If the features of biological sys-
tems and the opposition faced by distributed network sys-
tems are studied, it is tangible to make use of bio-inspired
techniques to solve these challenges[20]. Security is an as-
pect of concern everywhere, in nature and in the networking
environment. Biologically inspired approaches for providing
security in networks are interesting to evaluate because of
the analogies between network security and how the biotic
components react to perceived threats in their surroundings.
There have been many bio-inspired approaches used as a so-
lution to the attacks which take place in a wireless sensor
network, a few of which are reviewed below:
3.1 Swarm Intelligence (SI):
The basis for swarm intelligence is the behaviour of large
groups of collaborating small insects such as ants, bees or a
flock of birds. Simple and seemingly unrelated, separately
working individuals perform complex cooperative tasks co-
ordinating with each other in a parallel and distributed
manner[12]. Similar actions are required in networks and
computer science. Thus, swarm intelligence is being used as
a template for building self-organizing systems. The main
focus lies on the formation of groups or clusters that allow
efficient task allocation mechanisms[8].
Fig 2: Swarm Intelligence
3.2 Web Spider Defence
There are various types of web spiders, a few of which use
poison to paralyze their prey once it is trapped in the
web[6].
This behaviour of spider, used to capture a prey by building
a trap (web) can be translated into the field of networks for
apprehending an attacker. This is the technique used in
conventional honeypot methods.
Fig 3: Web Spider Defence algorithm[6]
3.3 Ant Colony Optimization (ACO)
This comes under swarm intelligence approach, uses the or-
ganization and food transporting capabilities in large ant
colonies and is widely used for solving any routing related
problems in wireless networks.
Fig 4: Ant Colony Optimization (ACO)
3.4 Artificial Immune System (AIS)

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The immune system in animals is the basis for artificial
immune system (AIS). The immune system in animals
reacts pro-actively, even to unknown attacks, and it is a
highly adaptive process. The primary goal of AIS, which is
inspired by the ethics and processes of the immune system,
is to effectively detect changes in the environment or
deviations from the normal system behaviour [9]. Therefore,
it makes sense to apply the same mechanisms for self-
organization and self-healing operations in computer
networks.
3.5 Artificial Neural Networks (ANN)
This system is based on the organizational principles used in
human brains[12].Artificial Neural Network (ANN) is a
mas- sively parallel computing systems consisting of large
number of interconnected simple processors to handle
various types of challenging computational problem such as
in wireless sensor network.
Fig 5: Artificial Neural Networks (ANN)
3.6 Human Immune System
Human Immune Systems are used as a prototype to cre- ate
comprehensive and precise Intrusion Detection Systems
(IDS).The inspiration for this method comes from the hu-
man body, which is composed of many cells. Out of these
cells the most important are the lymphocytes (white blood
cells) which have the capability to distinguish between self
and non-self (foreign cells)[13].
Fig 6: Human Immune System
4. SOLUTIONS TO SOME COMMON SE-
CURITY ATTACKS IN WSN
In this section, we have tabulated a few widely accepted
conventional and bio-inspired solutions to some of the
known security threats in a wsn. Conventional security
mechanisms like effective key management, cryptography,
authentication, Intrusion detection System (IDS) and
biological concepts such as ant colony optimization (ACO)
(used for secure routing), spider defence mechanism[6],
artificial immune system, artificial neural network are used.
The table below shows the various conventional and bio
inspired approaches that have been used to solve the attacks
that take place in the different layers of a OSI model.
5. COMPARISION BETWEEN
CONVENTIONAL AND BIO-INSPIRED
SOLUTION
5.1 Why a Bio-Inspired Solution?
When we talk about bio-inspired solution, we demonstrate a
strong relationship between the security attack and biology
through which we try to find a solution to the problem. The
indispensable question that follows is Why is it that we need
a bio-inspired solution? .The answer to this question lies in
the characteristics of the biological systems such as adapt-
ability, ability to learn and evolve when new conditions are
applied, ability to self-organize in a fully distributed fashion,
robustness[20]. And together with this there has also been a
paradigm shift in the development of computer networks
and have resulted in numerous challenges such as network
topology complexity, security among others. If one looks at
the characteristics of biological systems and the challenges
faced by distributed network systems, it is pretty evident
that one can apply bio-inspired techniques to solve these
challenges[20].
5.2 Conventional Solution vs Bio-Inspired Solution
The conventional methods, mostly rely on a central process-
ing unit (are centralized), and they depend on humans to be
programmed and told what to do (and how). This has some
very serious drawbacks. First, the systems are not very
robust. If one part of a system goes bad, the entire system
fails. Second, they are not adaptive. Most computing
systems cannot adjust or adapt to new or unexpected
situations without human intervention, unless mentioned in
the code. Third, scalability is a bare minimum.

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Table 1: summery of attacks in different layers and the security mechanisms used
Layer Attack Conventional Security mech-
anism
Bio-inspired methodology
Physical Jamming Direct Sequence Spread Spectrum
(DSSS), Frequency Hopping Spread
Spectrum (FHSS)[4]
Swarm Intelligence[3]
Physical Node capture Frequency Hopping Spread
Spectrum (FHSS)[2]
Artificial Neural Network[11]
Network, Data
Link
Sybil Resource Testing, Random key
predistribution[5]
BIOSARP Protocol based on Ant
Colony System[21],Human Immune
System.
Network Sinkhole Hop Count Monitoring method, RSSI
Based Scheme[25]
Artificial Immune System (AIS)
Network Wormhole Packet Leashes[10], CL-MAC
Protocol[1],LEACH Protocol[18]
Swarm Intelligence[19]
Transport Flooding Game
Theory[16],Authentication, IDS
Ant Colony Optimisation
(ACO)[4]
Transport, Physical,
Network
DoS Honey pot method, progressively
stronger authentication, IDS
Web Spider Defence Method[6]
Transport De-Synchronisation Authentication, Synchronization
Cookies
Artificial Neural Networks
Transport, Network Selective
Forwardingn
Support Vector Machines (SVM),
Intrusion Detection System (IDS)[23]
Human Immune System[20]
Network Hello Flood Attack Cryptography, signal strength and
client puzzles method[24]
Ant Colony System[21]
In contrast, bio-inspired (biological) methods/computing,
process information in a parallel and distributed way, with-
out the existence of a central control (decentralized). They
usually consist of a large number of relatively simple indi-
vidual units, each performing a part of a task. Example, the
brain consists of a large number of simple neurons that are
inter-connected, that process vast amounts of information.
Similarly, in insect colonies, such as ants, a large number of
relatively simple individuals manage to build ant hills or
find a food source, in a parallel and distributed way. This
parallel and distributed processing method makes these
systems highly robust. It is easy to maintain the system.
Further- more, these systems are highly scalable. Also, most
systems in nature are adaptive. They can adjust to changing
situations or even cope with entirely new situations.
6. CONCLUSION
In this paper, we have discussed about the importance of
providing security to wireless sensor networks. We catego-
rized and gave a summary of some common attacks that a
wireless sensor network encounters. A brief explana- tion
was also given for each attack. Few of the biological
methods/approaches that are used predominantly were re-
viewed. A comparison was made between conventional and
bio-inspired solutions, through which we have explained the
importance of bio-inspired algorithms for the optimal solu-
tions of wsn attacks. Bio-inspired algorithms have the dis-
tinctive features of being decentralized, bottom-up, adapt-
able, scalable and flexible, thus providing effective solutions
to problems that are otherwise restricted by limitations of
conventional methods.
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