Concealed Data Aggregation with Dynamic Intrusion Detection System to Remove Vulnerabilities in Wireless Sensor Networks

Natarajan Meghanathan et al. (Eds) : ACITY, VLSI, AIAA, CNDC - 2016
pp. 81–96, 2016. © CS & IT-CSCP 2016 DOI : 10.5121/csit.2016.60908
CONCEALED DATA AGGREGATION WITH
DYNAMIC INTRUSION DETECTION
SYSTEM TO REMOVE VULNERABILITIES
IN WIRELESS SENSOR NETWORKS
Bharat Bhushan1
, Keshav Kaushik2
and G Sahoo3
1,3
Department of Computer Engineering, BIT Mesra, Ranchi, India
Bharat_bhushan1989@yahoo.com
gsahoo@bitmesra.ac.in
2
Department of Computer Engineering, HMRITM, New Delhi, India
Keshavkaushik96@gmail.com
ABSTRACT
Data Aggregation is a vital aspect in WSNs (Wireless Sensor Networks) and this is because it
reduces the quantity of data to be transmitted over the complex network. In earlier studies
authors used homomorphic encryption properties for concealing statement during aggregation
such that encrypted data can be aggregated algebraically without decrypting them. These
schemes are not applicable for multi applications which lead to proposal of Concealed Data
Aggregation for Multi Applications (CDAMA). It is designed for multi applications, as it
provides secure counting ability. In wireless sensor networks SN are unarmed and are
susceptible to attacks. Considering the defence aspect of wireless environment we have used
DYDOG (Dynamic Intrusion Detection Protocol Model) and a customized key generation
procedure that uses Digital Signatures and also Two Fish Algorithms along with CDAMA for
augmentation of security and throughput. To prove our proposed scheme’s robustness and
effectiveness, we conducted the simulations, inclusive analysis and comparisons at the ending.
KEYWORDS
Concealed Data Aggregation, Digital Signatures, DYDOG, Wireless Sensor Networks.
1. INTRODUCTION
WIRELESS sensor networks (WSNs) have gained much significance in past few years because
of its huge number of applications and areas of use. The application domain ranges from military
investigations to environment monitoring and ecological monitoring. The sensor networks
generally comprises of several sensor nodes gathered from deployed environments in a large
scale [1]. Sensor nodes in sensor networks face a major problem as sensor nodes are energy
constrained and these have limited power, storage, communication, and processing capabilities.
Thus the major problem in wireless sensor network is energy consumption. Thus to conserve
energy and power sensor networks brings forth the concept of data aggregation [2]. This means

82 Computer Science & Information Technology (CS & IT)
converting many values sensed from different environments into one single value and aggregated
value is computed at sink by the use of some mathematical functions [3]. The technique for
aggregation is used mainly for the reduction in amount of data to be sent in the sensor
environments. As a result of reduction of amount of data communicated within WSNs, there is
energy conservation of battery [4]. Sensor nodes send their readings to a special type of node for
performing aggregation of data i.e., aggregators, that sends only the condensed or aggregated
reading further [5]-[6]. These aggregators may be some kind of special nodes or normal sensor
nodes also.
Sensor nodes requires high security as it prompts many security issues like confidentiality, data
integrity, data authentication, key management, etc. High security is required in wireless sensor
networks so it is one of the most popular research topics and much advancement have been
reported on in recent years[7]. In this paper we mainly focus on security aspect of data
transmission in WSNs and we propose a method of secure transmission of encrypted data across
sensor nodes in sensing environments as well as secure key generation methods involved in
attack detection and prevention in wireless sensor networks.
Encryption of data being transmitted in WSNs is necessary as this type of sensors can be subject
to many different types of attacks. The attacker can either listen secretly the data being
transmitted in WSNs (attacker may deduce the secret key) or send forged or duplicated data to
sensor nodes, aggregators or base station (attacker may send forged data to cheat BS without
knowing the secret key) or even compromise secrets of components of WSNs by capturing them.
so as encryption is necessary sensor nodes must encrypt data on hop-by-hop basis. [8]-[9]-[10]-
[11]. The mechanism of key generation involves an overhead activity making this an expensive
and complicated operation [13]-[14]. Different key generation schemes have been proposed but
they involve high computations for encryption of data and require more CPU, bandwidth and
memory. For this reason we in this paper are using digital signatures along with two fish
algorithm as a procedure for key generation[12].
2. DESIGN ISSUES AND ROUTING CHALLENGES IN WSN
Though the wireless sensor network have numerous applications, they have some restrictions like
limited energy supply, low bandwidth of links connecting sensor nodes or limited computational
power. The major goal of WSNs is to prolong the lifetime of the communication network and
also prevent the problems in connectivity by implementation of aggressive and efficient energy
management techniques. There are some challenging factors that influence the design of different
routing protocols in WSNs. Here in the following we present an overview of some of the routing
challenges as well as certain design issues that may affect the routing process in WSNs.
2.1. Node Deployment
This process of node deployment in WSNs is basically application dependent and it also affects
the routing performance. There are two ways of Deploying Nodes, namely deterministic and
randomized node deployment. The techniques in which sensors can be placed manually and data
routing are done through predefined paths are called deterministic deployment. However in the
case of random deployment the nodes are randomly scattered giving an impression of
infrastructure of ad-hoc distribution. Optimal clustering becomes necessary if the overall

Computer Science & Information Technology (CS & IT) 83
distribution of nodes is non-uniform simply to allow energy efficient routing or to allow
connectivity.
2.2. Energy Consumption
While performing computation or transmitting information’s, the sensor nodes use their energy
supply which is always limited in wireless environment. Thus the life of a sensor nodes shows a
heavy dependence on the network battery lifetime. Every node in a multihop WSN plays dual
role, both as a data sender and data router. Significant topological changes can be caused by
power failures which may be a result of malfunctioning of certain sensor nodes. This may require
the reorganization and rerouting of the packets being permitted in the network.
2.3. Node or Link Heterogeneity
Generally the sensor nodes are considered to be homogenous that is they all have some capacity
in terms of communication, computation and power. But sometimes sensor nodes may have
different capabilities or roles depending on their application as some of the applications may
require a blend of sensors for monitoring pressure, temperature and humidity of the environment
capturing the image or tracking of mobile objects. These special types of sensor nodes can either
be independently deployed or all the different functionalities could be included in some sensor
nodes. For example some hierarchical protocols can designate a cluster head to be different from
normal sensor nodes. The cluster heads are chosen from among deployed sensors and are more
powerful than the normal sensor nodes in terms of memory, energy and bandwidth. This handles
the burden of transmission.
2.4. Fault Tolerance and Scalability
The failure of certain sensor nodes due to physical damage or lack of power or environment
interface should not affect the overall performance of the sensor network. There may be a
problem when many nodes may fail, the MAC and the routing protocols should accommodate the
formation of new links or new routers to the data collecting BS. Thus multilevel redundancy id
required in a fault tolerant sensor network. Scalability is another issue in WSN as the number of
SN deployed in any sensing area they may be in the order of thousands or even more. The routing
scheme used must be capable of working with the huge number of sensor nodes. Also the routing
protocols being used must scalable enough to respond to any event or operation in the
environment. Most of the sensors remain in sleep state until an event occurs.
2.5. Connectivity and Coverage
Higher node density in any sensor network prevent them from being isolated from each other.
Thus the sensor nodes are expected to be well connected. This may not prevent different network
topologies from being variable and also from the network Size shrinking due to failure of some
sensor nodes. Thus the connectivity depends on the random distribution of sensor nodes. In
WSNs all the sensor nodes obtain their own view of sensing environment. This view of sensor is
limited both in accuracy as well as in range. Thus only a limited physical sensing area can be
covered. Thus Area coverage also becomes an important design parameter in WSN.

3. PRELIMINARIES
3.1. Privacy Homomorphism Encryption
An encryption scheme with homomorphic property is privacy homomorphism encryption. The
homomorphic property means that the algebraic operations on PT can be executed with the
manipulation of the corresponding CT with the help of a key.
Dk (Ek (m1) O Ek (m2) = m1 @ m2,
Where Dk ( ) is decryption with key K., Ek ( ) is encryption with key K, O denote operations on
cipher text and @ denote operations on plaintext.
PH schemes are of two types, similar to conventional encryption schemes. First one is Symmetric
cryptosystems where keys are identical and second one is Asymmetric cryptosystem where keys
are different. Symmetric PH schemes have greater efficiency as compared to Asymmetric PH
schemes. The best known Asymmetric schemes are the one based on ECC (Elliptic Curve
Cryptography) which provides the same security as RSA cryptosystem and that too with a
smaller key size and cipher text. A 160-bit ECC cryptosystem provides the same security as
provided by a 1,024-bit RSA cryptosystem [24].
3.2. Data Aggregation and Encryption
There is a major problem of aggregation of encrypted data in WSNs [23] which was firstly
introduced by gira et al. in [10] and it was further refined in [15] .Homomorphic encryption
schemes was used to enable arithmetic operations over cipher texts that is to be transmitted on a
multi-hop basis. Secure aggregation also involves some problems with public-key encryption
mechanisms [16]-17]. Solution to public key encryption mechanism is to equip nodes with
private keys for increasing the security level. This limits the effect of attacker that compromises
some of the nodes but this is not deployed yet because of certain reasons mainly being the high
computational cost involved in encryption and decryption of plaintext and cipher texts. Also the
expansion in bit size during plaintext to cipher text conversion involves high overhead hence
depleting the sensors energy.
3.3. Routing Protocols
The efficiency of a sensor networks heavily depends on the routing protocols used. Energy
Efficient & Secure Pattern Based Data Aggregation protocol (ESPDA) was proposed that
considered data aggregation and security together for wireless sensor networks [18]. In ESPDA
cluster heads prevent transmission of redundant data from sensor nodes making ESPDA as
energy and bandwidth efficient. Next concept was Secure Reference Based Data Aggregation
(SRDA) in which the raw sensed data by sensor nodes is compared with referenced data values
and the only the differential data is transmitted rather than the raw data [19]. Hein Zelman, et al.
[20] proposed a hierarchical clustering algorithm for sensor networks. This was Low Energy
Adaptive Cluster Hierarchy (LEACH) based protocol. Here the operations were divided into
rounds and during each round another set of nodes acts as CHs. Main advantage of this was that
energy consumption is uniformly distributed among all the nodes and the main disadvantage was
that it uses scheduling criteria based on (TDMA) time division multiple access which makes it

inclined to long delays when it is applied to large sensor networks. An enhancement over
LEACH protocol was published in [21]. This protocol was PEGASIS (Power Efficient Gathering
in Sensor Information Systems). It was a chain based protocol designed for extending the lifetime
of the network which elects a leader from the chain, based on residual energy level which results
in average energy spent by each node being reduced. Virtual Grid Architecture (VGA) was
another energy efficient routing paradigm proposed in [22]. This protocol used data aggregation
and also in network processing to maximize the lifetime of the network as it performs data
aggregation at two levels: local and global. PEGASIS greatly prolongs the lifetime of network
when transmission range is limited and VGA saves more energy when transmission range is
more.
3.4. CDA Based Privacy Homomorphism Schemes
Our work focusses on the solution for confidential data exchanges in WSNs that incorporates
data aggregation. To the best of our knowledge, CDA (Concealed Data Aggregation) was the first
concept that proposed a solution for end-to-end encryption along with the data aggregation
model. In [8], the basic idea of CDA was introduced and it also showed the way to apply privacy
homomorphism in WSNs. CDA provides end-to-end security along with providing in-network
processing. They use algebraic properties of the applied PH: additive and multiplicative PH. In
recent years, Castellucia, et al. Introduced an efficient data aggregation of encrypted data in
WSNs and this is also based on additive homomorphism of encryption scheme [15]. Next concept
introduced was CDAMA where the private keys is kept secret and it is only known by the base
station. There is same public key for SNs in same group and no one outside knows the public key
of the group. Also here BS extracts individual aggregated results from aggregated CT by
performing individual decryption.
3.5. Digital Signatures
In some hostile environments, like a military battlefield, or environment monitoring, the
broadcast authentication is an essential requirement to ensure the security and privacy of a
Wireless Sensor Networks. The best way to provide broadcast authentication is to use digital
signatures in sensor networks [25]. Public-key cryptography is considered to be too expensive for
small sensor nodes, because it (like RSA) requires extensive computations and generally is not
suitable for tiny sensors. In order to achieve Message integrity, Message Authentication Codes
(MACs) are used. It allows for the application of digital Signatures that provide Data integrity
and repudiation. Only the party that has the private key can create a particular signature. When a
message along with a signature is received, only then the corresponding public key is used to
verify the signature and once the signature verification is done, the receiver knows message
integrity is still preserved. Digital signature is the most critical security services that
cryptography offers. Digital Signature is an authentication mechanism which enables the sender
to attach a unique code (signature). The signature is generated by taking the hash of the message
and then encrypting the message with the sender’s private key. It is an NIST standard that uses
secure hash algorithm. The plaintext message, the message signature and the Public Key of
sender are bundled together and transformed into signed and encrypted message using the Public
Key of the receiver. The receiver unbundles received message and computes the message digest
of the received message that is compared to the decrypted signature.

4. MODULE DESIGN
4.1. WSN set-up Model
In this module we set up a WSN environment in which network is divided into static clusters
containing SN. Sensor nodes having limited energy and secure communication among them are
necessary. Aggregator nodes are chosen based on residual energy level of nodes. Each sensor
nodes sends the sensed data to corresponding aggregators which aggregates the received value
and transfers the aggregated result to Base Station BS. We assume the Base Station to have
immense computational power so it generates two types of keys, both public and private keys for
sensor nodes using CDAMA scheme. All sensors have common public key but different private
keys. Now the generated key is assigned to all the sensor nodes.
4.2. Aggregation Model
In WSN information is collected by sensor nodes from deployed environments and this collected
information is forwarded to base station via multi-hop transmission based on cluster topology.
This accumulated transmission results in high energy consumption for the intermediate nodes.
Thus to increase the lifetime of the sensor networks cluster topology enables the intermediate
nodes to perform data aggregation(AG).After performing aggregation AGs forwards the
aggregated result to next hop. Aggregation of data takes place by two methods i.e., algebraic
operations (e.g., adding or multiplying) or statistical operations (e.g., mean, median, mode, max,
min). AG forwards only the aggregated result instead of forwarding the entire raw data.
4.3. Attack Model
Here in this model, we create two unauthorized sensor nodes called the attacker nodes which
have more energy and threshold as compared to the normal nodes. There are different types of
possible attacks on WSNs. Here we in this paper are considering the DOS attack Denial-of-
Service attack which causes Black hole attack, Wormhole attack, Sybil attacks, Selective
forwarding attacks etc. DOS attack is based on node-id. The attacker node behaves as normal
nodes with its changed node id and receives data packets and drops them causing loss of data.
Attacker nodes also change the threshold of the normal nodes thus drying the energy of the
normal nodes. There are two methods followed by the attacker nodes here. Firstly, it traces the
node id and changes the node id (based on node id) and secondly changes the threshold value
(based on energy level).
4.4. Security Model
To ensure Data integrity and Data Confidentiality homomorphic encryption is used, which allows
operations on encrypted data without decrypting them at the intermediate nodes thus preventing
the access to plaintext. Considering the security issues in WSNs, we use Dynamic Intrusion
Detection Protocol model (DYDOG) where Dynamic Intrusion Detection nodes are deployed
which acts as both forwarding node and also intrusion monitoring and detection nodes with
respect to data flow through sensor network. It uses secure session key management technique
without deploying separate intrusion monitoring nodes. This technique makes network more
dynamic and flexible against various kinds of attacks. Here in our work, we have used DYDOG

technique along with a secure key generation scheme that is based on Digital Signatures and Two
Fish Algorithms.
5. SYSTEM DESCRIPTION AND SECURITY OBJECTIVES
5.1. Network Architecture and Operating Mechanism
In this paper, we consider a wireless sensor network system consisting of a fixed base station and
large number of sensor nodes. These sensor nodes are homogenous in functionalities as well as
capabilities. We suppose, the sink as reliable always , but the sensor nodes are subject to be
compromised by the attackers. In this wireless system, the data are sensed by the sensor nodes
and are transmitted to a base station with the help of CHs that performs data aggregation. We also
assume that, all sensor nodes and the BS use the symmetric radio channel, sensor nodes are
distributed randomly, and are energy constrained. The protocol used is CDAMA that elects CHs,
and a sensor node transmits the data to its CH.
5.2. Security Vulnerabilities and Objectives
Like all other type routing protocols in WSNs, CDAMA are vulnerable to number of security
attacks e.g., jamming, spoofing, replay attacks, etc. Because of depending on the CHs for data
aggregation and routing procedure, attacks involving CHs could be the serious to the network
system. If an attacker compromises the secrets of a CH, it can provoke attacks such as sinkhole
or selective forwarding attacks, thereby degrading the network performance. Also the attacker
may intend to inject any forged sensing data into the network towards the CHs. If we use
DYDOG and Digital Signatures along with the normal CDAMA protocol then we notice lesser
attacks and improved network performance on the basis of throughput, packet delivery ratio, and
throughput.
5.3. Protection from Vulnerabilities Mechanism
Wireless sensor networks are vulnerable to attacks like Denial Of Service (DOS) which includes
mainly Black hole attack, Wormhole attack, Sybil attack, Jamming attacks and selective
forwarding attacks. This is a serious problem in WSN. A packet drop attack or black hole attack
is a type of DOS attack in which a node that is supposed to relay packets actually discards them
instead. This occurs when a node is compromised by the attacker. Because the packets are
dropped from a lossy network, it is very hard to detect and prevent these packet drops. The
adversary can make several compromised nodes in Black hole intercepted region. Intruder can
also sense or read the secret data from. Similarly wormhole attack records and also uses the secret
data in unauthorized manner, Sybil attack causes faulty identification and the Selective
forwarding attack data loss in wireless sensor networks. Against these various types of attacks
Dydog model provides flexible and resilient solution by Dynamic Intrusion Detection Nodes for
High-Data rate. Dynamic Intrusion Detection Protocol model (DYDOG) Design is based on data
flow in Wireless Sensor Networks (WSNs). In this the Dynamic Intrusion Detection nodes are
deployed which will act as forwarding node as well as an Intrusion Monitoring Node. The
selection of dynamic intrusion detection nodes are from the neighbor’s non-forwarding node list
using Secure Session Key Management approach without deploying any separate Intrusion
Monitoring Nodes. Because of this the network becomes more dynamic and flexible against

various types of attacks and provides availability of maximum monitoring node’s with high error
rate Wireless Networks.
Within the session itself depending on mobility the monitoring nodes dynamically change its
behavior. For an attacker it creates problem to identify and attack these nodes within the limited
session. By this technique the attacks and the Compromised nodes can be effectively and easily
identified at runtime even in high data rate dynamic or static Wireless Sensor Networks (WSNs).
6. OUR SCHEME
We have used DYDOG mechanism for security enhancement of CDAMA. First we performed
the Wormhole attack on CDAMA technique. Wormhole acts by three procedures.
1) Message duplication
2) Compromising the node-id of normal nodes
3) Packet dropping
To overcome this wormhole attack we use DYDOG mechanism, Digital signatures and Two fish
algorithm.
Steps involved are as follows:
6.1. Key Generation Procedure
1. If source is transmitting data
2. Count the number of requests
3. Evaluate N [expr($len_q1)*($len_$q2)*($len_$q3)]
4. Initialize E
5. Randomize GEN as value of index.
6. Evaluate H = [(q1)*(q2)]*GEN
7. Evaluate Tmax = [(T)/(x)]
8. Evaluate P = [(q2)*(q3)*(GEN)]
9. Find Public key [ ((N)*(E)*(P)*(H)*(Tmax))]
10. Return Public key
6.2. Encryption Procedure
1. If data is received at destination
2. Count the number of reply
3. If request= message_id then randomize the value of R
4. Calculate cipher text as per expression C = [(M)*(P)+(R)*(H)]
5. Return the value of ciphertext as C.
6. Calculate aggregation count AGG_C=[(Message_id *P) + (Message_id *Q) +
($Message_id *H)]
7. Return the value of AGG_C

6.3. Aggregation Procedure
1. Compute the aggregated result as cipher text C’=C1+C2. It also includes the randomness
of both groups.
2. Return C’
6.4. Decryption Procedure
1. Compute M,M = logp (q2q3*C)
2. Return M
6.5. DyDog Two Fish and Digital Signature Procedure
In this the Dynamic Intrusion Detection nodes are deployed which will act as forwarding node as
well as an Intrusion Monitoring Node.
We define the following apps for using DyDOG along with Digital Signatures and Two Fish
algorithm.
6.5.1. Cryptographic App
1. Initialize Crypto App with new app.
2. Use the catch variable in a procedure, if catch has crypto app and it should load the path
of library where cryptography is available else it should return the error code.
3. Using the class namespace, eval , tcl drop , two fish and encryption is accessed. All will
be accessed in the name space.
4. Variable of two fish version is specified i.e., version 0.1.
5. Package is provided for accessing the two fish and tcl drop and for calling the version
initialized above
6. Package is provided for accessing the encryption and tcl drop
7. If the information is available for accessing the numversion of tcl return the same
8. Call the check module function for encryption
9. Define three procedures with keywords (Encrypt, Decrypt and Encrypt Password).
6.5.2. Cipher Text App
1. initialize cipher app with value new_app
2. if twofish.tcl file is available(file will be generated when running the code and disappears
once executed) take it as source
3. check the packages required for Itcl, Tcl 8.4, tcltest,itwofish
4. proc h2b {hex}
return [binary format H* $hex] //return the expression
5. proc b2h {bin}
binary scan $bin H* dummy //scan binary value and make it as dummy value
return $dummy //returning dummy value

6. If length of argument value is equal to 0 for each statement with digital signature number
and cipher, then increment the testnum
7. Initialize the engine with two fish and digital signature number
8. Initialize encrypted value of engine in the encrypted block with the value of h2b and
clear
9. Initialize the decrypted value of engine in the decrypted block with the value of
encrypted which is obtained above.
10. if no string is encrypted using b2h or if no string is decrypted using b2h then reject the
increment else approve the increment
11. Initialize a file in open status and trim the string and perform Monte Carlo test.
12. If the value of input vector and Monte Carlo value is equal create another engine using
two fish and digital signature number.
6.5.3. Tests
We perform three tests here.
i. CBC mode test:
1. Initialize the engine with two fish and digital signature number.
2. Encrypt h2b (two fish object value) and pt (variable name) and make it encrypted with
engine value.
3. Configure the engine with h2b (two fish object value).
4. Decrypt the encrypted value with engine and store it in decrypted app.
5. Access the itcl and delete the engine else call the value and check the below test
ii. Monte Carlo test
2. Initialize the data with h2b (two fish object value) and pt (variable name).
3. Initialize the data with engine and encrypt block with data and make data encrypted.
4. Initialize the data with engine and decrypt block with data and make data decrypted.
5. Access the itcl and delete the engine else check the below test.
iii. ECB mode test
2. Initialized encrypted value with engine and encrypted block with h2b (twofish object).
3. Initialized decrypted value with engine and decrypted block with called encrypted value.
4. Access the itcl and delete the engine.
5. If encrypted text does not match expectation or decrypted text does not match original
plaintext then increment is failed otherwise approve the increment.
6. Initialize t with some message and encrypt the same with e and the decryption of block
should be done while calling e.
6.5.4 Message App
1. Calling self to view the messages by node

2. Globally declaring ns with digital signature without encryption
3. Merge the messages viewed with respect to the message id’s
4. Trace the node and node address
5. Calling self to send the size of message id and data with encrypt port.
6. Check the number of nodes and assign the node values to i.
7. Append the node id and message and assign Digital Signatures to all the nodes.
6.5.5 Check and Channel App
1. Check the value of packet size and if the expression of node boundary values is perfect
with the data node,it will start transmiting the request otherwise node transmit stop
2. Assign each packet to channel and call the watch dog procedure and send it to all the
channels.
6.5.6 Request
1. Creating new application called request.
2. Set flag as 0 and if source transmits data and reply from the destination is equal to source
received reply then start the data transmission.
3. Else update the request and transmit the request till it reached the maximum value and
initialize till request is maximum
6.5.7 Transmit
1. Create a new application called transmit
2. If source has the data received and the data received is equal to request, assign flag as 0
3. If flag is 0, drop the request, else transmit the request
4. If destinations have the received data and if node id request and node id reply are same
then initialize the reply again
6.5.8 Reply
1. Create a new application called reply.
2. If reply is received and the received reply is not equal to source, assign flag as 1.
3. If flag is 0, transmit reply or acknowledgement or data and assign flag as 1 else drop the
request.
4. Else assign flag as 1 and if reply received to node is not equal to source, transmit new
reply or acknowledgement or data and assign flag as 1.
Count the values if node transmits are equal to 0 and if count is less than 2 then transmit count
else stop the reply.
7. SIMULATION PARAMETER
This model is implemented using a network simulator 2.34. The simulation parameters are 500 X
500 sq. area, and consisted of 50 to 60 number of nodes with flat-grid topology, two ray ground
ground radio propagation model and 802.15.4 MAC layer .AODV, CDAMA, AODV under
Attack, CDAMA under attack and CDAMA along with DYDOG and Digital Signatures from
different perspectives such as Average-delay, Packet Delivery ratio, Energy Spent and

Throughput. The network simulator set up is shown below in the table.
TABLE I SIMULATION PARAMETERS
SI. No. PARAMETERS Values
1 Simulation area 500 X 500 square meters
2 Propagation Two ray ground propagation
3 Queue type Drop tail
4 Antenna type Omni antenna
5 Number of nodes 50 to 60 nodes
6 Topology Flat grid topology
7 Routing protocol CDAMA
8 Maximum packets
in interface queue
length
200
9 Network interface
type
Phy/wireless
10 MAC type 802.11
8. SIMULATION RESULTS AND DISCUSSIONS
8.1. Average Delay
Average delay includes all the possible types of delays that may be either due to buffering during
route discovery latency, or queuing at the interface queue or may be the transfer times of the data
packets. The figure shows the end to end delay incurred in transferring the data from source node
to sink node by different routing schemes. The maximum delay is in AODV with attack, Sybill
attack followed by the CDAMA with attack. In an efficient network the average delay should be
less and when CDAMA is compared to AODV under attack and CDAMA under attack, it has
lesser delay but when we incorporate DYDOG along with Digital signatures the delay is further
reduced.
Fig.1. Variation of average delay with time

8.2. Packet Delivery Ratio
Packet Delivery Ratio is the ratio of the data packets that has been delivered to destinations to
those that has been generated by constant bit rate (CBR) sources. The figure shows the packet
delivery ration achieved by different routing techniques. The packet delivery ratio is highest for
CDAMA enhanced technique followed by the normal CDAMA technique. In case of CDAMA
under attack the number of packets sent is high but received number of packets is less so the PDR
decreases for the AODV and CDAMA under attack.
Fig.2. Variation of Packet delivery Ratio with Time
8.3. Energy Consumption
Average Energy Consumption by the sensor nodes in the network is one of the most important
metrics to evaluate energy efficiency of the routing protocol that has been proposed. The figure
shows the energy spent by nodes in the sensor network. Energy consumption for CDAMA
technique is lesser than enhanced CDAMA because of some additional procedures like Two fish
algorithms, Digital signatures and Dydog mechanisms. These extra procedures result in more
energy consumption for enhanced cdama. Though DYDOG consumes little more energy but it
also increases the throughput
Fig.3. Variation of energy consumption with time

8.4. Throughput
Throughput is the total number of routing packets transmitted per data packets that has been
delivered at destination. The throughput of CDAMA with DYDOG mechanism is more because
of digital signatures along with Two fish algorithm. Though energy consumption is more but as
throughput of overall transmission increases and hence the security also increases.
Fig.4. variation of Throughput with time.
9. CONCLUSION
The work proposes a secure, increased throughput and a better packet delivery ration scheme than
normal CDAMA technique. Here the Dynamic Intrusion Detection Protocol (DYDOG) model is
used along with Digital Signatures along with Two Fish Algorithms for CDAMA technique
where cipher text of different applications can be aggregated together. While using these
algorithms and protocols we have enhanced the working of CDAMA technique that mitigates the
impact and reduces the overall damage to acceptable condition. CDAMA performs better than the
traditional AODV routing protocol but the proposed technique provides higher security using
Digital Signatures along with two Fish algorithm. The proposed technique defends the altered
routing, selective forwarding and wormhole attacks. In this paper the energy consumption is still
a issue as this technique leads to more energy consumption by the sensor nodes In our future
work we will be proposing a technique for lesser energy consumption than the proposed
technique.
ACKNOWLEDGEMENTS
The authors would like to thank everyone, just everyone!
REFERENCES
[1] I. Akyildiz,W. Su,Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks” IEEE
Comm. Magazine, vol. 40, no. 8, pp. 102-114, Aug. 2002.
[2] R. Min,A. Chandrakasan, “Energy-Efficient Communication for Ad-Hoc Wireless Sensor Networks,”
Proc. Conference Record of the 35th Asilomar Conference Signals, Systems and Computers, vol. 1,
2001.

[3] B. Przydatek, D. Song,,A. Perrig, “SIA: Secure Informations Aggregation in Sensor Networks,” Proc.
First International Conf. Embedded Network Sensor Systems, pp. 255-265, 2003.
[4] R.Chandramouli, S.Bapatla, and K.P.Subbalakshmi, ”Battery power-aware encryption.ACM
transactions on information and system security,” pp. 162-180, 2006.
[5] K.Akkaya,M.Demirbas, RS.Aygun, “The Impact Of Data Aggregation on the performance of
Wireless Sensor Networks,” wiley wireless Communication Mobile Computing (WCMC), J(8), 171-
193, 2008.
[6] Alzaid,H, Foo,E, and Nieto J.G, “ Secure data aggregation in wireless sensor network-:a survey,” In
the proceedings of the sixth Australasian conference on information security, Volume-81, pp.93-105,
2008.
[7] J.Girao, M. Schneider, and D.Westhoff, “CDA:Concealed Data Aggregation in wireless sensor
networks,”Proceedings of the ACM workshop on Wireless Security, 2004.
[8] D. Westhoff, J. Girao, and M. Acharya, “Concealed Data Aggregation for Reverse Multicast Traffic
in Sensor Networks: Encryption, Key Distribution, and Routing Adaptation,” IEEE Trans. Mobile
Computing, vol. 5, no. 10, pp. 1417-1431, Oct. 2006.
[9] E. Mykletun, J. Girao, and D. Westhoff, “Public Key Based Crypto schemes for Data Concealment in
Wireless Sensor Networks,” Proc. IEEE Int’l Conf. Comm. (ICC ’06), vol. 5, 2006.
[10] J. Girao, D. Westhoff, M. Schneider, N. Ltd, and G. Heidelberg, “CDA: Concealed Data Aggregation
for Reverse Multicast Traffic in Wireless Sensor Networks,” Proc. IEEE Int’l Conf. Comm. (ICC
’05), vol. 5, 2005.
[11] J. Girao, D. Westhoff, E. Mykletun, and T. Araki, “Tinypeds:Tiny Persistent Encrypted Data Storage
in Asynchronous Wireless Sensor Networks,”Ad Hoc Networks, vol. 5, no. 7, pp. 1073-1089, 2007.
[12] C.d. Westhoff,B.Lamparter, and A.Weimerskirch,“on digital signaturesin ad hoc networks,”
J.Eur.Trans. telecom, vol.16, no. 5, pp. 411-425, 2005.
[13] R.Watro, D.Kong, S.Cuti, C.Gardiner, C.lynn, and P.kruus, “Sensor networks with public key
technology”, in proc. 2nd ACM Workshop Security ad hoc sensor network, pp.59-64, 2004.
[14] C.karlof ,D.Wagner, ”Secure routing in wireless sensor networks: Attacks and countermeasures,”in
the proc IEEE Int. Workshop Sensor Netw. Protocols Appl., May 2003, pp. 113-127, May 2003.
[15] C.castelluccia, E.Mykletun and G.Tsudik, “Efficient Aggregation of encrypted data in wireless sensor
networks,” Mobile and Ubiquitous Systems: Networking and Services, 2005.
[16] E. Mykletun, J. Girao, and D. Westhoff, "Public Key Based Cryptoschemes for Data Concealment in
Wireless Sensor Networks", IEEE Int'l Conf. Communications (ICC '06), pp. 2288-2295, 2006.
[17] T.ELGamal, “A Public Key Cryptosystem and a Signature Scheme based on Discrete Logarithms,”
CRYPTO,IT-31(4): pp. 469-472, 1985.
[18] H. Cam, S. O¨zdemir, P. Nair, D. Muthuavinashiappan, and H.O. Sanli, “Energy-Efficient Secure
Pattern Based Data Aggregation for WSNs,” Computer Comm., vol. - 29, no. - 4, pp. 446-455, 2006.

[19] H. Sanli, S. Ozdemir, and H. Cam, “SRDA: Secure Reference based Data Aggregation Protocol for
WSNs,” Proc. IEEE 60th Vehicular Technology Conf. (VTC ’04-fall), vol. 7, 2004.
[20] M. Younis,M.Youssef and K.Arisha,”Energy Aware Routing in Cluster Based Sensor Networks”, in
the Proceedings of the 10th IEEE/ACM(MASCOTS2002), Fort Worth, TX , October 2002.
[21] S.Lindsay and C.Raghavendra, ”PEGASIS: Power Efficient gathering in Sensor Info.
Systems”,international conference on communications, 2001.
[22] J.N.Al-Karaki,et al., “data Aggregation in Wireless Sensor Networks-Exact and approximate
algorithms,” Proc IEEE Wks. High Perf. Switching and Routing 2004, phoenix, AZ , Apr.18-
21,2004.
[23] Sanjeev Setia, a. Sankardas Roy and Sushil Jajodi “Secure Data Aggregation in Wireless Sensor
Networks” Proc. of 33rd STOC, pp. 266–275, 2001.
[24] N. Koblitz, A. Menezes,S., Vanstone,“State of Elliptic Curve Cryptography,”Designs, Codes &
Cryptography, vol. 19, no. 2, pp. 173-193, 2000.
[25] Cryptography and Network Security Principles and practices, William Stallings, Pearson Education,
Fifth Edition.
AUTHORS
Bharat Bhushan (M’26).Date Of Birth-17th Dec 1989. Phd Scholar ( Dept. of
Computer Sc. & Engg.) student at Birla Institute of Technology, Ranchi, Jharkhand-
835215, India.He has worked as Network Engineer for 1 years in HCL Infosystems
Ltd., Noida
Keshav Kaushik (M’20). Date Of birth- 21/05/1996 BTech Computer Science &
engineering student at HMR institute of technology & management.
.
Gadadhar Sahoo (M’60) Professor and Dean (Admissions and Academic
Coordination) , Dept. Of Computer Science & Engg., Ph.D.., IIT Kharagpur. He has
working experience of teaching field of 26 years and research experience of 31 years.
He is now with Birla Institute Of Technology, Mesra, Ranchi.He has 167 publications
in Natinal/International journals and 80 publications in National/International
Conferences.Dr. Sahoo is also a member of ISTE.

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