Using Association Rules for Fraud Detection in Web Advertising Networks

Summary: Streaming-Rules reports association rules in data streams with tight error guarantees, using small per-item processing. Modular design fits stream engines, targeting fraud in web advertising networks, showing scalability and fraud discovery. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9300
Venue: VLDB
Year: 2005
Pagerank: 5.1725247e-05
Overall Rank: 12,571 | 12.64%
DOI: -

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
12,397	SLEUTH: Single-publisher attack detection Using correlation Hunting	2008	VLDB	5.1725247e-05

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD	0.00066546358
119	Approximate Frequency Counts over Data Streams	2002	VLDB	0.00032265625
162	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00028238267
813	What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically	2003	PODS	0.00013906993
4,631	False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams	2004	VLDB	6.6577421e-05
6,220	A Regression-Based Temporal Pattern Mining Scheme for Data Streams	2003	VLDB	6.0117255e-05

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12,397	SLEUTH: Single-publisher attack detection Using correlation Hunting	2008	VLDB	5.1725247e-05
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