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.
v 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.
v 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.
- 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.
- Setting up Network Model
- Robust Data Aggregation
- Enhanced Iterative Filtering
- Accuracy with a Collusion Attack
Setting up Network Model
Our first module is setting up the network model. We consider a large-scale, homogeneous sensor network consisting of resource-constrained sensor nodes. The sensor nodes are divided into disjoint clusters, and each cluster has a cluster head which acts as an aggregator. Data are periodically collected and aggregated by the aggregator. We assume that each data aggregator has enough computational power to run an IF algorithm for data aggregation.
Robust Data Aggregation
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 first iteration of the IF algorithm. Most of the traditional statistical estimation methods for variance involve use of the sample mean. For this reason, proposing a robust variance estimation method in the case of skewed sample mean is an essential part of our methodology. We assume that the stochastic components of sensor errors are independent random variables with a Gaussian distribution; however, our experiments show that our method works quite well for other types of errors without any modification. Moreover, if error distribution of sensors is either known or estimated, our algorithms can be adapted to other distributions to achieve an optimal performance. Based on such an estimation of the bias and variance of each sensor, the bias estimate is subtracted from sensors readings and in the next phase of the proposed framework, we provide an initial estimate of the reputation vector calculated using the MLE.
Enhanced Iterative Filtering
According to the proposed attack scenario, the attacker exploits the vulnerability of the IF algorithms which originates from a wrong assumption about the initial trustworthiness of sensors. Our contribution to address this shortcomings is to employ the results of the proposed robust data aggregation technique as the initial reputation for these algorithms. Moreover, the initial weights for all sensor nodes can be computed based on the distance of sensors readings to such an initial reputation. Our experimental results illustrate that this idea not only consolidates the IF algorithms against the proposed attack scenario, but using this initial reputation improves the efficiency of the IF algorithms by reducing the number of iterations needed to approach a stationary point within the prescribed tolerance
Accuracy with a Collusion Attack
In order to illustrate the robustness of the proposed data aggregation method in the presence of sophisticated attacks, we synthetically generate several data sets by injecting the proposed collusion attacks. Therefore, we assume that the adversary employs c (c < n) compromised sensor nodes to launch the sophisticated attack scenario proposed. The attacker uses the first compromised nodes to generate outlier readings in order to skew the simple average of all sensor readings. The adversary then falsifies the last sensor readings by injecting the values very close to such skewed average. This collusion attack scenario makes the IF algorithm to converge to a wrong stationary point. In order to investigate the accuracy of the IF algorithms with this collusion attack scenario, we synthetically generate several data sets with different values for sensors variances as well as various number of compromised nodes. The results of this experiment validate that our sophisticated attack scenario is caused by the discovered vulnerability in the IF algorithms which sharply diminishes the contributions of benign sensor nodes when one of the sensor nodes reports a value very close to the simple average.
- System : Pentium IV 2.4 GHz.
- Hard Disk : 40 GB.
- Floppy Drive : 1.44 Mb.
- Monitor : 15 VGA Colour.
- Mouse : Logitech.
- Ram : 512 Mb.
- Operating system : Windows XP/7.
- Coding Language : JAVA/J2EE
- IDE : Netbeans 7.4
- Database : MYSQL
Click Here To Download