Secure Data Aggregation in Wireless Sensor Networks: Filtering out the Attacker S Impact

Secure Data Aggregation in Wireless Sensor Networks: Filtering out the Attacker S Impact

Secure Data Aggregation in Wireless Sensor Networks: Filtering out the Attacker’s Impact

Abstract:-

Due to a need for robustness of monitoring, wireless sensor networks (WSN) are usually redundant. Data from multiple sensors is aggregated at an aggregator node which then forwards to the base station only the aggregate values. At present, due to limitations of the computing power and energy resource of sensor nodes, data is aggregated by extremely simple algorithms such as averaging. However, such aggregation is known to be very vulnerable to faults, and more importantly, malicious attacks. This cannot be remedied by cryptographic methods, because the attackers generally gain complete access to information stored in the compromised nodes. For that reason data aggregation at the aggregator node has to be accompanied by an assessment of trustworthiness of data from individual sensor nodes. Thus, better, more sophisticated algorithms are needed for data aggregation in the future WSN. Such an algorithm should have two important features. we present an algorithm to enable the base station to securely compute predicate Count or Sum even in the presence of such an attack. Our attack-resilient computation algorithm computes the true aggregate by filtering out the contributions of compromised nodes in the aggregation hierarchy. Thorough theoretical analysis and extensive simulation study show that our

algorithm outperforms other existing approaches

Existing System:-

In order to improve the performance of IF algorithms against the aforementioned

attack scenario, we provide a robust initial estimation of the trustworthiness of sensor nodes to be used in the rst iteration of the IF algorithm. Most of the traditional statistical estimation methods for variances involve use of the sample mean. For this reason, proposing a robust variance estimation method in the case of skewed sample mean is essential part of our methodology. Thus, in this section we propose a novel attacker detection technique in order to further diminish the impact of the compromised nodes. We will describe our proposed collusion detection scheme and then discuss the proposed compromised nodes revocation approach.

Proposed:-

Iterative Filtering (IF) algorithms are an attractive option for WSNs because they solve both problems - data aggregation and data trustworthiness assess- ment - using a single iterative procedure. Such trustworthiness estimate of each sensor is based on the distance of the readings of such a sensor from the estimate of the correct values, obtained in the previous round of iteration by some form of aggregation of the readings of all sensors. Such aggregation is usually a weighted average; sensors whose readings signicantly dier from such estimate are assigned less trustworthiness and consequently in the aggregation process in the present round of iteration their readings are given a lower weight. Our goal is to enable BS to obtain the ‘true’ estimate of the aggregate (which BS would compute if there were no compromised nodes) even in the presence of the attack. More formally, goal (a) is to detect if ˆB, the synopsis received at BS is the same as the ‘true’ final synopsis B, and goal (b) is to compute B from ˆB and other received information. Without loss of generality, we present our algorithms in the context of Sum aggregate. As Count is a special case of Sum, where each node reports a unit value, these algorithms are readily applicable to Count aggregate also.

System Requirements:

Hardware Requirements:

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Floppy Drive: 1.44 Mb.

• Monitor: 15 VGA Color.

• Mouse: Logitech.

• Ram: 512 Mb.

S/W System Configuration:-

Operating System : Windows XP / 7

Front End : JAVA,RMI, SWING