Learning decision trees using the Fourier spectrum
SIAM Journal on Computing
Empirically derived analytic models of wide-area TCP connections
IEEE/ACM Transactions on Networking (TON)
Randomized algorithms
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Tracking join and self-join sizes in limited storage
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast, small-space algorithms for approximate histogram maintenance
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Estimating Rarity and Similarity over Data Stream Windows
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Internet intrusions: global characteristics and prevalence
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
An Approximate L1-Difference Algorithm for Massive Data Streams
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Stable distributions, pseudorandom generators, embeddings and data stream computation
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Automatically inferring patterns of resource consumption in network traffic
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Bitmap algorithms for counting active flows on high speed links
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
Comparing data streams using Hamming norms (how to zero in)
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Finding hierarchical heavy hitters in data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
High-throughput sketch update on a low-power stream processor
Proceedings of the 2006 ACM/IEEE symposium on Architecture for networking and communications systems
Finding hierarchical heavy hitters in network measurement system
Proceedings of the 2007 ACM symposium on Applied computing
Coordinated weighted sampling for estimating aggregates over multiple weight assignments
Proceedings of the VLDB Endowment
NADA - network anomaly detection algorithm
DSOM'07 Proceedings of the Distributed systems: operations and management 18th IFIP/IEEE international conference on Managing virtualization of networks and services
Exponential time improvement for min-wise based algorithms
Information and Computation
sub-space clustering and evidence accumulation for unsupervised network anomaly detection
TMA'11 Proceedings of the Third international conference on Traffic monitoring and analysis
Get the most out of your sample: optimal unbiased estimators using partial information
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
What's the difference?: efficient set reconciliation without prior context
Proceedings of the ACM SIGCOMM 2011 conference
Exponential time improvement for min-wise based algorithms
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
User subjectivity in change modeling of streaming itemsets
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Proceedings of the 7th International Conference on Network and Services Management
Unsupervised Network Intrusion Detection Systems: Detecting the Unknown without Knowledge
Computer Communications
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Anomaly extraction in backbone networks using association rules
IEEE/ACM Transactions on Networking (TON)
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Monitoring and analyzing network traffic usage patterns is vital for managing IP Networks. An important problem is to provide network managers with information about changes in traffic, informing them about "what's new." Specifically, we focus on the challenge of finding significantly large differences in traffic: over time, between interfaces and between routers. We introduce the idea of a deltoid: an item that has a large difference, whether the difference is absolute, relative or variational.We present novel algorithms for finding the most significant deltoids in high-speed traffic data, and prove that they use small space, very small time per update, and are guaranteed to find significant deltoids with pre-specified accuracy. In experimental evaluation with real network traffic, our algorithms perform well and recover almost all deltoids. This is the first work to provide solutions capable of working over the data with one pass, at network traffic speeds.