16 research outputs found
Unsupervised Dictionary Learning for Anomaly Detection
We investigate the possibilities of employing dictionary learning to address
the requirements of most anomaly detection applications, such as absence of
supervision, online formulations, low false positive rates. We present new
results of our recent semi-supervised online algorithm, TODDLeR, on a
anti-money laundering application. We also introduce a novel unsupervised
method of using the performance of the learning algorithm as indication of the
nature of the samples.Comment: in Proceedings of iTWIST'20, Paper-ID: 09, Nantes, France, December,
2-4, 202
Community-Level Anomaly Detection for Anti-Money Laundering
Anomaly detection in networks often boils down to identifying an underlying
graph structure on which the abnormal occurrence rests on. Financial fraud
schemes are one such example, where more or less intricate schemes are employed
in order to elude transaction security protocols. We investigate the problem of
learning graph structure representations using adaptations of dictionary
learning aimed at encoding connectivity patterns. In particular, we adapt
dictionary learning strategies to the specificity of network topologies and
propose new methods that impose Laplacian structure on the dictionaries
themselves. In one adaption we focus on classifying topologies by working
directly on the graph Laplacian and cast the learning problem to accommodate
its 2D structure. We tackle the same problem by learning dictionaries which
consist of vectorized atomic Laplacians, and provide a block coordinate descent
scheme to solve the new dictionary learning formulation. Imposing Laplacian
structure on the dictionaries is also proposed in an adaptation of the Single
Block Orthogonal learning method. Results on synthetic graph datasets
comprising different graph topologies confirm the potential of dictionaries to
directly represent graph structure information
Fault Handling in Large Water Networks with Online Dictionary Learning
Fault detection and isolation in water distribution networks is an active
topic due to its model's mathematical complexity and increased data
availability through sensor placement. Here we simplify the model by offering a
data driven alternative that takes the network topology into account when
performing sensor placement and then proceeds to build a network model through
online dictionary learning based on the incoming sensor data. Online learning
is fast and allows tackling large networks as it processes small batches of
signals at a time and has the benefit of continuous integration of new data
into the existing network model, be it in the beginning for training or in
production when new data samples are encountered. The algorithms show good
performance when tested on both small and large-scale networks.Comment: Accepted Journal of Process Contro
Quick survey of graph-based fraud detection methods
In general, anomaly detection is the problem of distinguishing between normal
data samples with well defined patterns or signatures and those that do not
conform to the expected profiles. Financial transactions, customer reviews,
social media posts are all characterized by relational information. In these
networks, fraudulent behaviour may appear as a distinctive graph edge, such as
spam message, a node or a larger subgraph structure, such as when a group of
clients engage in money laundering schemes. Most commonly, these networks are
represented as attributed graphs, with numerical features complementing
relational information. We present a survey on anomaly detection techniques
used for fraud detection that exploit both the graph structure underlying the
data and the contextual information contained in the attributes
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization
This article presents a leak localization methodology based on state
estimation and learning. The first is handled by an interpolation scheme,
whereas dictionary learning is considered for the second stage. The novel
proposed interpolation technique exploits the physics of the interconnections
between hydraulic heads of neighboring nodes in water distribution networks.
Additionally, residuals are directly interpolated instead of hydraulic head
values. The results of applying the proposed method to a well-known case study
(Modena) demonstrated the improvements of the new interpolation method with
respect to a state-of-the-art approach, both in terms of interpolation error
(considering state and residual estimation) and posterior localization
Data-driven leak localization in water distribution networks via dictionary learning and graph-based interpolation
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification. The former estimates the complete WDN hydraulic state (i.e., hydraulic heads) from real measurements at certain nodes and the network graph. Then, we append to the actual measurements a subset of relevant estimated states to feed and train the dictionary learning scheme. Thus, the meshing of these two methods is explored, and several promising performance results are attained, even deriving different mechanisms to increase the resilience to classical issues (e.g., dimensionality, interpolation errors, etc.). The approach is validated using the L-TOWN benchmark proposed in the BattLeDIM2020 competition.Peer ReviewedPostprint (author's final draft