Secure data aggregation technique for wireless

Secure Data Aggregation Technique for Wireless Sensor
Networks in the Presence of Collusion Attacks
ABSTRACT:
Due to limited computational power and energy resources, aggregation of data
from multiple sensor nodes done at the aggregating node is usually accomplished
by simple methods such as averaging. However such aggregation is known to be
highly vulnerable to node compromising attacks. Since WSN are usually
unattended and without tamper resistant hardware, they are highly susceptible to
such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor
nodes is crucial for WSN. As the performance of very low power processors
dramatically improves, future aggregator nodes will be capable of performing more
sophisticated data aggregation algorithms, thus making WSN less vulnerable.
Iterative filtering algorithms hold great promise for such a purpose. Such
algorithms simultaneously aggregate data from multiple sources and provide trust
assessment of these sources, usually in a form of corresponding weight factors
assigned to data provided by each source. In this paper we demonstrate that several
existing iterative filtering algorithms, while significantly more robust against
collusion attacks than the simple averaging methods, are nevertheless susceptive to
a novel sophisticated collusion attack we introduce. To address this security issue,
we propose an improvement for iterative filtering techniques by providing an

initial approximation for such algorithms which makes them not only collusion
robust, but also more accurate and faster converging.
EXISTING SYSTEM:
 In recent years, there has been an increasing amount of literature on IF
algorithms for trust and reputation systems. The performance of IF
algorithms in the presence of different types of faults and simple false data
injection attacks has been studied where it was applied to compressive
sensing data in WSNs.
 In the past literature it was found that these algorithms exhibit better
robustness compared to the simple averaging techniques; however, the past
research did not take into account more sophisticated collusion attack
scenarios. If the attackers have a high level of knowledge about the
aggregation algorithm and its parameters, they can conduct sophisticated
attacks on WSNs by exploiting false data injection through a number of
compromised nodes.

DISADVANTAGES OF EXISTING SYSTEM:
 Although the existing IF algorithms consider simple cheating behaviour by
adversaries, none of them take into account sophisticated malicious
scenarios such as collusion attacks.
 Although the existing IF algorithms consider simple cheating behaviour by
adversaries, none of them take into account sophisticated malicious
scenarios such as collusion attacks.
PROPOSED SYSTEM:
 This paper presents a new sophisticated collusion attack scenario against a
number of existing IF algorithms based on the false data injection. In such
an attack scenario, colluders attempt to skew the aggregate value by forcing
such IF algorithms to converge to skewed values provided by one of the
attackers.
 In this paper, we propose a solution for vulnerability by providing an initial
trust estimate which is based on a robust estimation of errors of individual
sensors.
 Identification of a new sophisticated collusion attack against IF based
reputation systems which reveals a severe vulnerability of IF algorithms.
 A novel method for estimation of sensors’ errors which is effective in a wide
range of sensor faults and not susceptible to the described attack.

 Design of an efficient and robust aggregation method inspired by the MLE,
which utilises an estimate of the noise parameters obtained using
contribution above.
 Enhanced IF schemes able to protect against sophisticated collusion attacks
by providing an initial estimate of trustworthiness of sensors using inputs
from contributions
ADVANTAGES OF PROPOSED SYSTEM:
 We provide a thorough empirical evaluation of effectiveness and efficiency
of our proposed aggregation method. The results show that our method
provides both higher accuracy and better collusion resistance than the
existing methods.
 To the best of our knowledge, no existing work addresses on false data
injection for a number of simple attack scenarios, in the case of a collusion
attack by compromised nodes in a manner which employs high level
knowledge about data aggregation algorithm used.

SYSTEM ARCHITECTURE:
BLOCK DIAGRAM:

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech.
 Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
 Operating system : Windows XP/7/LINUX.
 Implementation : NS2
 NS2 Version : NS2.2.28
 Front End : OTCL (Object Oriented Tool Command
Language)
 Tool : Cygwin (To simulate in Windows OS)

REFERENCE:
Mohsen Rezvani, Student Member, IEEE, Aleksandar Ignjatovic, Elisa Bertino,
Fellow, IEEE, and Sanjay Jha, Senior Member, IEEE, “Secure Data Aggregation
Technique for Wireless Sensor Networks in the Presence of Collusion Attacks”,
IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING,
VOL. 12, NO. 1, JANUARY/FEBRUARY 2015.

Secure data aggregation technique for wireless

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