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Large-scale distributed monitor networks are in face of the challenge of tremendous data communication costs due to the resource restriction. Prediction models can be used to reduce communication cost over the networks. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still...
IRC botnet can be regarded as a collection of compromised computers (called Zombie computers) running software under the command-and-control infrastructure constructed by the IRC server. The connection between the botnet server and the bots are usually very dynamic. In order to describe a botnet at a finer granularity, the paper proposed a method that identify homologous botnets by extracting communication...
Processing multi-extreme value queries efficiently over data streams is important for data analysis in real-time environment. Cost-efficient processing of continuous extreme values queries over sliding windows, especially about resource sharing, is considered. Firstly, an effective storage structure to minimize the number of elements to be kept for queries is given. We prove the average the cardinality...
Conficker is a worm outbreak recently which form a large botnet and became a huge threat to the security of the internet. In this paper, the redirect technology of domain name was used to monitor the conficker. For it's low killing rate and long-term period of propagation, we built a propagation model of botnet based on conficker monitoring. In the model, we take into account the geography, connectivity...
Events detection is one of the most important issues in data stream processing. Outlier event, change event and burst event are three typical types of event that need to be identified. In this paper, we explore the relationship of these three types of events, and then present a unified framework, named DSED (distributed stream events detection), to handle all of them simultaneously. Rather than collecting...
We study the problem of filtering duplicate items over physically distributed data streams to provide clean data for real-time monitoring applications. Existing approaches only filter local duplicates within each stream, and their space and time costs are hardly feasible for high-speed data streams. Based on the space/time efficient data structure Bloom filter, we propose a novel local filtering algorithm...
Emerging hardware and communication technologies enable new data stream applications that deal efficiently with very high rates of data updates. In this paper, we propose a novel index structure, termed the QDM-tree (quantized R*-tree with double memos), to support efficient similarity search over data streams. We integrate quantized minimum bounding spheres (QMBSs) and quantized minimum bounding...
Burst detection is an inherent problem for data streams, so it has attracted extensive attention in research community due to its broad applications. One of the basic problems in burst detection is how to count frequencies of all elements in data stream. This paper presents a novel solution based on Improved Counting Bloom Filter, which is also called BCBF+HSet. Comparing with intuitionistic approach...
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