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Book ; Online: Predicting the structure of dynamic graphs

Kandanaarachchi, Sevvandi

2024

Abstract: Dynamic graph embeddings, inductive and incremental learning facilitate predictive tasks such as node classification and link prediction. However, predicting the structure of a graph at a future time step from a time series of graphs, allowing for new ... ...

Abstract	Dynamic graph embeddings, inductive and incremental learning facilitate predictive tasks such as node classification and link prediction. However, predicting the structure of a graph at a future time step from a time series of graphs, allowing for new nodes has not gained much attention. In this paper, we present such an approach. We use time series methods to predict the node degree at future time points and combine it with flux balance analysis -- a linear programming method used in biochemistry -- to obtain the structure of future graphs. Furthermore, we explore the predictive graph distribution for different parameter values. We evaluate this method using synthetic and real datasets and demonstrate its utility and applicability.
Keywords	Computer Science - Machine Learning ; Computer Science - Social and Information Networks ; Statistics - Machine Learning
Publishing date	2024-01-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Unsupervised Anomaly Detection Ensembles using Item Response Theory

Kandanaarachchi, Sevvandi

2021

Abstract: Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the response variable or the class labels ... ...

Abstract	Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods is challenging because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the response variable or the class labels cannot be used to construct an ensemble for unsupervised anomaly detection. We use Item Response Theory (IRT) -- a class of models used in educational psychometrics to assess student and test question characteristics -- to construct an unsupervised anomaly detection ensemble. IRT's latent trait computation lends itself to anomaly detection because the latent trait can be used to uncover the hidden ground truth. Using a novel IRT mapping to the anomaly detection problem, we construct an ensemble that can downplay noisy, non-discriminatory methods and accentuate sharper methods. We demonstrate the effectiveness of the IRT ensemble on an extensive data repository, by comparing its performance to other ensemble techniques. Comment: 25 pages
Keywords	Statistics - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2021-06-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Short‐term prediction of stream turbidity using surrogate data and a meta‐model approach: A case study

Rele, Bhargav / Hogan, Caleb / Kandanaarachchi, Sevvandi / Leigh, Catherine

Hydrological Processes. 2023 Apr., v. 37, no. 4 p.e14857-

2023

Abstract: Many water‐quality monitoring programs aim to measure turbidity to help guide effective management of waterways and catchments, yet distributing turbidity sensors throughout networks is typically cost prohibitive. To this end, we built and compared the ... ...

Abstract	Many water‐quality monitoring programs aim to measure turbidity to help guide effective management of waterways and catchments, yet distributing turbidity sensors throughout networks is typically cost prohibitive. To this end, we built and compared the ability of dynamic regression (auto‐regressive integrated moving average [ARIMA]), long short‐term memory neural nets (LSTM), and generalized additive models (GAM) to forecast stream turbidity one step ahead, using surrogate data from relatively low‐cost in‐situ sensors and publicly available databases. We iteratively trialled combinations of four surrogate covariates (rainfall, water level, air temperature and total global solar exposure) selecting a final model for each type that minimized the corrected Akaike information criterion. Cross‐validation using a rolling time‐window indicated that ARIMA, which included the rainfall and water‐level covariates only, produced the most accurate predictions, followed closely by GAM, which included all four covariates. However, according to the no‐free‐lunch theorems in machine learning, no single model has an advantage over all other models for all instances. Therefore, we constructed a meta‐model, trained on time‐series features of turbidity, to take advantage of the strengths of each model over different time points and predict the best model (that with the lowest forecast error one‐step prior) for each time step. The meta‐model outperformed all other models, indicating that this methodology can yield high accuracy and may be a viable alternative to using measurements sourced directly from turbidity‐sensors where costs prohibit their deployment and maintenance, and when predicting turbidity across the short term. Our findings also indicated that temperature and light‐associated variables, for example underwater illuminance, may hold promise as cost‐effective, high‐frequency surrogates of turbidity, especially when combined with other covariates, like rainfall, that are typically measured at coarse levels of spatial resolution.
Keywords	air temperature ; case studies ; cost effectiveness ; hydrology ; neural networks ; prediction ; rain ; streams ; time series analysis ; turbidity ; water quality
Language	English
Dates of publication	2023-04
Publishing place	John Wiley & Sons, Inc.
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	1479953-4
ISSN	1099-1085 ; 0885-6087
ISSN (online)	1099-1085
ISSN	0885-6087
DOI	10.1002/hyp.14857
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Comparison of new computational methods for spatial modelling of malaria.

Wong, Spencer / Flegg, Jennifer A / Golding, Nick / Kandanaarachchi, Sevvandi

Malaria journal

2023 Volume 22, Issue 1, Page(s) 356

Abstract: Background: Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, ... ...

Abstract	Background: Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases. Methods: This work presents an applied comparison of four proposed 'fast' computational methods for spatial modelling and the software provided to implement them-Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales-country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation. Results: Two of these methods-SpRF and GPBoost-do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods-INLA and FRK-do scale well computationally, however the resulting model fits are very sensitive to the user's modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution. Conclusions: INLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters.
MeSH term(s)	Humans ; Models, Statistical ; Computer Simulation ; Software ; Spatial Analysis ; Malaria/epidemiology ; Bayes Theorem
Language	English
Publishing date	2023-11-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2091229-8
ISSN	1475-2875 ; 1475-2875
ISSN (online)	1475-2875
ISSN	1475-2875
DOI	10.1186/s12936-023-04760-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Detecting inner-LAN anomalies using hierarchical forecasting

Kandanaarachchi, Sevvandi / Abolghasemi, Mahdi / Ochiai, Hideya / Rao, Asha

2023

Abstract: Increasing activity and the number of devices online are leading to increasing and more diverse cyber attacks. This continuously evolving attack activity makes signature-based detection methods ineffective. Once malware has infiltrated into a LAN, ... ...

Abstract	Increasing activity and the number of devices online are leading to increasing and more diverse cyber attacks. This continuously evolving attack activity makes signature-based detection methods ineffective. Once malware has infiltrated into a LAN, bypassing an external gateway or entering via an unsecured mobile device, it can potentially infect all nodes in the LAN as well as carry out nefarious activities such as stealing valuable data, leading to financial damage and loss of reputation. Such infiltration could be viewed as an insider attack, increasing the need for LAN monitoring and security. In this paper we aim to detect such inner-LAN activity by studying the variations in Address Resolution Protocol (ARP) calls within the LAN. We find anomalous nodes by modelling inner-LAN traffic using hierarchical forecasting methods. We substantially reduce the false positives ever present in anomaly detection, by using an extreme value theory based method. We use a dataset from a real inner-LAN monitoring project, containing over 10M ARP calls from 362 nodes. Furthermore, the small number of false positives generated using our methods, is a potential solution to the "alert fatigue" commonly reported by security experts.
Keywords	Computer Science - Cryptography and Security
Publishing date	2023-04-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: DEFT

Kaluarachchi, Nuwan / Kandanaarachchi, Sevvandi / Moore, Kristen / Arakala, Arathi

A new distance-based feature set for keystroke dynamics

2023

Abstract: Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. ... ...

Abstract	Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices. Comment: 12 pages, 5 figures, 3 tables, conference paper
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-10-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Spatio-temporal spread of artemisinin resistance in Southeast Asia.

Flegg, Jennifer A / Kandanaarachchi, Sevvandi / Guerin, Philippe J / Dondorp, Arjen M / Nosten, Francois H / Otienoburu, Sabina Dahlström / Golding, Nick

PLoS computational biology

2024 Volume 20, Issue 4, Page(s) e1012017

Abstract: Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria- ... ...

Abstract	Current malaria elimination targets must withstand a colossal challenge-resistance to the current gold standard antimalarial drug, namely artemisinin derivatives. If artemisinin resistance significantly expands to Africa or India, cases and malaria-related deaths are set to increase substantially. Spatial information on the changing levels of artemisinin resistance in Southeast Asia is therefore critical for health organisations to prioritise malaria control measures, but available data on artemisinin resistance are sparse. We use a comprehensive database from the WorldWide Antimalarial Resistance Network on the prevalence of non-synonymous mutations in the Kelch 13 (K13) gene, which are known to be associated with artemisinin resistance, and a Bayesian geostatistical model to produce spatio-temporal predictions of artemisinin resistance. Our maps of estimated prevalence show an expansion of the K13 mutation across the Greater Mekong Subregion from 2000 to 2022. Moreover, the period between 2010 and 2015 demonstrated the most spatial change across the region. Our model and maps provide important insights into the spatial and temporal trends of artemisinin resistance in a way that is not possible using data alone, thereby enabling improved spatial decision support systems on an unprecedented fine-scale spatial resolution. By predicting for the first time spatio-temporal patterns and extents of artemisinin resistance at the subcontinent level, this study provides critical information for supporting malaria elimination goals in Southeast Asia.
MeSH term(s)	Artemisinins/pharmacology ; Asia, Southeastern/epidemiology ; Drug Resistance/genetics ; Antimalarials/pharmacology ; Humans ; Bayes Theorem ; Spatio-Temporal Analysis ; Plasmodium falciparum/drug effects ; Plasmodium falciparum/genetics ; Mutation ; Malaria/drug therapy ; Malaria/epidemiology ; Computational Biology ; Malaria, Falciparum/drug therapy ; Malaria, Falciparum/parasitology ; Malaria, Falciparum/epidemiology
Chemical Substances	Artemisinins ; Antimalarials ; artemisinin (9RMU91N5K2)
Language	English
Publishing date	2024-04-16
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1012017
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Early classification of spatio-temporal events using partial information.

Kandanaarachchi, Sevvandi / Hyndman, Rob J / Smith-Miles, Kate

PloS one

2020 Volume 15, Issue 8, Page(s) e0236331

Abstract: This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event ... ...

Abstract	This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material.
MeSH term(s)	Algorithms ; Big Data ; Conservation of Natural Resources ; Data Mining/methods ; Environmental Monitoring/methods ; Fiber Optic Technology/methods ; Nitrogen Dioxide/analysis
Chemical Substances	Nitrogen Dioxide (S7G510RUBH)
Language	English
Publishing date	2020-08-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0236331
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Honeyboost

Kandanaarachchi, Sevvandi / Ochiai, Hideya / Rao, Asha

Boosting honeypot performance with data fusion and anomaly detection

2021

Abstract: With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. The ability of Network Anomaly Detection Systems (NADS) to identify unusual behavior makes them useful in predicting ... ...

Abstract	With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. The ability of Network Anomaly Detection Systems (NADS) to identify unusual behavior makes them useful in predicting such attacks. However, NADS often suffer from high false positive rates. In this paper, we introduce a novel framework called Honeyboost that enhances the performance of honeypot aided NADS. Using data from the LAN Security Monitoring Project, Honeyboost identifies most anomalous nodes before they access the honeypot aiding early detection and prediction. Furthermore, using extreme value theory, we achieve the highly desirable low false positive rates. Honeyboost is an unsupervised method comprising two approaches: horizontal and vertical. The horizontal approach constructs a time series from the communications of each node, with node-level features encapsulating their behavior over time. The vertical approach finds anomalies in each protocol space. Using a window-based model, which is typically used in online scenarios, the horizontal and vertical approaches are combined to identify anomalies and gain useful insights. Experimental results indicate the efficacy of our framework in identifying suspicious activities of nodes. Comment: 26 pages
Keywords	Computer Science - Cryptography and Security ; Statistics - Applications
Subject code	006
Publishing date	2021-05-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Images

Kandanaarachchi, Sevvandi / Gardner, Wil / Alexander, David L. J. / Muir, Benjamin W. / Chouinard, Philippe A. / Crewther, Sheila G. / Scurr, David J. / Halliday, Mark / Pigram, Paul J.

Analytical Chemistry. 2023 Nov. 14, v. 95, no. 47 p.17384-17391

2023

Abstract: Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, ... ...

Abstract	Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.
Keywords	analytical chemistry ; data collection ; discriminant analysis ; mass spectrometry ; microarray technology ; surveys
Language	English
Dates of publication	2023-1114
Size	p. 17384-17391.
Publishing place	American Chemical Society
Document type	Article ; Online
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.3c03887
Database	NAL-Catalogue (AGRICOLA)

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