Efficient Heavy Hitters Identification Over Speed Traffic Streams
نویسندگان
چکیده
منابع مشابه
Hashing Pursuit for Online Identification of Heavy-Hitters in High-Speed Network Streams
Distributed Denial of Service (DDoS) attacks have become more prominent recently, both in frequency of occurrence, as well as magnitude. Such attacks render key Internet resources unavailable and disrupt its normal operation. It is therefore of paramount importance to quickly identify malicious Internet activity. The DDoS threat model includes characteristics such as: (i) heavy-hitters that tra...
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Aggregation along hierarchies is a critical summary technique in a large variety of online applications including decision support, and network management (e.g., IP clustering, denial-of-service attack monitoring). Despite the amount of recent study that has been dedicated to online aggregation on sets (e.g., quantiles, hot items), surprisingly little attention has been paid to summarizing hier...
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We consider applications scenarios where an untrusted aggregator wishes to continually monitor the heavy-hitters across a set of distributed streams. Since each stream can contain sensitive data, such as the purchase history of customers, we wish to guarantee the privacy of each stream, while allowing the untrusted aggregator to accurately detect the heavy hitters and their approximate frequenc...
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An old and fundamental problem in databases and data streams is that of finding the heavy hitters, also known as the top-k, most popular items, frequent items, elephants, or iceberg queries. There are several variants of this problem, which quantify what it means for an item to be frequent, including what are known as the `1-heavy hitters and `2-heavy hitters. There are a number of algorithmic ...
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Modern data streams typically have high dimensionality. For example, digital analytics streams consist of user online activities (e.g., web browsing activity, commercial site activity, apps and social behavior, and response to ads). An important problem is to nd frequent joint values (heavy hiers) of subsets of dimensions. Formally, the data stream consists of d-dimensional items and a k-dime...
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ژورنال
عنوان ژورنال: Computers, Materials & Continua
سال: 2020
ISSN: 1546-2226
DOI: 10.32604/cmc.2020.07496