Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Cluster based data aggregation in vehicular adhoc network

Abstract: Information aggregation is used to fuse interrelated data items collected from different nodes before redistributing them. Thus, using aggregation the number of transmissions and the communication overhead can be reduced significantly. Particularly for applications which require periodic dissemination of information into a large region, aggregation becomes prerequisite. Let us consider an example of self-organized infrastructure less traffic information system where each vehicle periodically disseminates its speed, position and information about road conditions. The aim is to design such data aggregation technique that not only use low bandwidth for data dissemination after aggregation process but also use low bandwidth for data collection from source nodes as well. As VANET environment has limited bandwidth this data aggregation becomes the need of hour for efficient data processing and dissemination without network overhead. In this work we have proposed a cluster based data aggregation technique where vehicles are divided into autonomous clusters headed by cluster-head. Data is send to cluster-head that perform the aggregation of data and dissimulate it into network.

Securing Node Capture Attacks for Hierarchical Data Aggregation in Wireless Sensor Networks

Abstract: Serious security threat is originated by node capture attacks in hierarchical data aggregation where a hacker achieves full control over a sensor node through direct physical access in wireless sensor networks. It makes a high risk of data confidentiality. In this study, we propose a securing node capture attacks for hierarchical data aggregation in wireless sensor networks. Initially network is separated into number of clusters, each cluster is headed by an aggregator and the aggregators are directly connected to sink. The aggregator upon identifying the detecting nodes selects a set of nodes randomly and broadcast a unique value which contains their authentication keys, to the selected set of nodes in first round of data aggregation. When any node within the group needs to transfer the data, it transfers slices of data to other nodes in that group, encrypted by individual authentication keys. Each receiving node decrypts, sums up the slices and transfers the encrypted data to the aggregator. The aggregator aggregates and encrypts the data with the shared secret key of the sink and forwards it to the sink. The set of nodes is reselected with new set of authentication keys in the second round of aggregation. By simulation results, we demonstrate that the proposed technique resolves the security threat of node capture attacks.

Secure and energy-efficient data aggregation in Wireless Sensor Networks

Abstract: Data aggregation in intermediate nodes (called aggregator nodes) is an effective approach for optimizing consumption of scarce resources like bandwidth and energy in Wireless Sensor Networks (WSNs). However, in-network processing poses a problem for the privacy of the sensor data since individual data of sensor nodes need to be known to the aggregator node before the aggregation process can be carried out. In applications of WSNs, privacy-preserving data aggregation has become an important requirement due to sensitive nature of the sensor data. Researchers have proposed a number of protocols and schemes for this purpose. He et al. (INFOCOM 2007) have proposed a protocol—called CPDA—for carrying out additive data aggregation in a privacy-preserving manner for application in WSNs. The scheme has been quite popular and well-known.

Result-Based Detection of Insider Threats to Relational Databases

Abstract: Insiders misuse of resources is a real threat to organizations. According to recent security reports, data has been the most vulnerable to attacks by insiders, especially data located in databases and corporate file servers. Although anomaly detection is an effective technique for flagging early signs of insider attacks, modern techniques for the detection of anomalies in database access are not able to detect several sophisticated data misuse scenarios such as attempts to track data updates and the aggregation of data by an insider that exceeds his/her need to perform job functions. In such scenarios, if the insider does not have prior knowledge of the distribution of the target data, many of his/her queries may extract no data or small amounts of data. Therefore, monitoring the total size of data retrieved by each user and comparing it to normal levels will either result in low anomaly detection accuracy or long time to anomaly detection. In this paper, we propose anomaly detection techniques designed to detect data aggregation and attempts to track data updates. Our techniques infer the normal rates of tables references and tuples retrievals from past database access logs. User queries are then analyzed to detect queries that lead to exceeding the normal data access rates. We evaluated the proposed techniques on the query logs of a real database. The results of the evaluation indicate that when the system configuration parameters are adequately selected and sufficient data is available for training, our techniques have low false alarm generation rate and high anomaly detection accuracy.