96 results found
Mikhailova A, Adams N, Hallsworth C, et al., Unsupervised deep learning for instrumented infrastructure: a case study, International Conference on Smart Infrastructure and Construction
Lau D-H, Adams NM, The importance of analysing data from instrumented infrastructure, International Conference on Smart Infrastructure and Construction
Ward S, Cohen E, Adams N, Fusing multimodal microscopy data for improved cell boundary estimation and fluorophore localization of Pseudomonas aeruginosa, Asilomar Conference on Signals, Systems and Computers
Plasse J, Adams N, 2019, Multiple changepoint detection in categorical data streams, Statistics and Computing, ISSN: 0960-3174
The need for efficient tools is pressing in the era of big data, particularly in streaming data applications. As data streams are ubiquitous, the ability to accurately detect multiple changepoints, without affecting the continuous flow of data, is an important issue. Change detection for categorical data streams is understudied, and existing work commonly introduces fixed control parameters while providing little insight into how they may be chosen. This is ill-suited to the streaming paradigm, motivating the need for an approach that introduces few parameters which may be set without requiring any prior knowledge of the stream. This paper introduces such a method, which can accurately detect changepoints in categorical data streams with fixed storage and computational requirements. The detector relies on the ability to adaptively monitor the category probabilities of a multinomial distribution, where temporal adaptivity is introduced using forgetting factors. A novel adaptive threshold is also developed which can be computed given a desired false positive rate. This method is then compared to sequential and nonsequential change detectors in a large simulation study which verifies the usefulness of our approach. A real data set consisting of nearly 40 million events from a computer network is also investigated.
Bakoben M, Bellotti A, Adams N, Identification of credit risk based on cluster analysis of account behaviours, Journal of the Operational Research Society, ISSN: 0160-5682
Assessment of risk levels for existing credit accounts isimportant to the implementation of bank policies and offeringfinancial products.This paper uses cluster analysis of be-haviour of credit card accounts to help assess credit risk level.Account behaviour is modelled parametrically and we thenimplement the behavioural cluster analysis using a recentlyproposed dissimilarity measure of statistical model parameters.The advantage of this new measure is the explicit exploitationof uncertainty associated with parameters estimated fromstatistical models.Interesting clusters of real credit cardbehaviours data are obtained, in addition to superior predictionand forecasting of account default based on the clusteringoutcomes.
Lau FD-H, Adams NM, Girolami MA, et al., 2018, The role of statistics in data-centric engineering, STATISTICS & PROBABILITY LETTERS, Vol: 136, Pages: 58-62, ISSN: 0167-7152
Noble J, Adams NM, 2018, Real-Time Dynamic Network Anomaly Detection, IEEE INTELLIGENT SYSTEMS, Vol: 33, Pages: 5-18, ISSN: 1541-1672
Lau D, Butler L, Adams N, et al., Real-time Statistical Modelling of Data Generated from Self-Sensing Bridges, Proceedings of the Institution of Civil Engineers - Civil Engineering, ISSN: 0965-089X
Hogan J, Adams NM, 2018, A Study of Data Fusion for Predicting Novel Activity in Enterprise Cyber-Security, 16th Annual IEEE International Conference on Intelligence and Security Informatics (IEEE ISI), Publisher: IEEE, Pages: 37-42
Riddle-Workman E, Evangelou M, Adams NM, 2018, Adaptive Anomaly Detection on Network Data Streams, 16th Annual IEEE International Conference on Intelligence and Security Informatics (IEEE ISI), Publisher: IEEE, Pages: 19-24
Hoagn J, Cohen E, Adams N, 2017, Devising a fairer method for adjusting target scores in interrupted one-day international cricket, ELECTRONIC JOURNAL OF APPLIED STATISTICAL ANALYSIS, Vol: 10, Pages: 745-758, ISSN: 2070-5948
Bodenham DA, Adams NM, 2017, Continuous monitoring for changepoints in data streams using adaptive estimation, STATISTICS AND COMPUTING, Vol: 27, Pages: 1257-1270, ISSN: 0960-3174
Schon C, Adams N, Evangelou M, 2017, Clustering and Monitoring Edge Behaviour in Enterprise Network Traffic, 15th IEEE International Conference on Intelligence and Security Informatics - Security and Big Data (ISI), Publisher: IEEE, Pages: 31-36
Bakoben M, Bellotti A, Adams N, 2016, Improving clustering performance by incorporating uncertainty, PATTERN RECOGNITION LETTERS, Vol: 77, Pages: 28-34, ISSN: 0167-8655
Bodenham DA, Adams NM, 2016, A comparison of efficient approximations for a weighted sum of chi-squared random variables, STATISTICS AND COMPUTING, Vol: 26, Pages: 917-928, ISSN: 0960-3174
Adams N, Heard N, 2016, Dynamic networks and cyber-security, ISBN: 9781786340740
© 2016 by World Scientific Publishing Europe Ltd. All rights reserved. As an under-studied area of academic research, the analysis of computer network traffic data is still in its infancy. However, the challenge of detecting and mitigating malicious or unauthorised behaviour through the lens of such data is becoming an increasingly prominent issue. This collection of papers by leading researchers and practitioners synthesises cutting-edge work in the analysis of dynamic networks and statistical aspects of cyber security. The book is structured in such a way as to keep security application at the forefront of discussions. It offers readers easy access into the area of data analysis for complex cyber-security applications, with a particular focus on temporal and network aspects. Chapters can be read as standalone sections and provide rich reviews of the latest research within the field of cyber-security. Academic readers will benefit from state-of-the-art descriptions of new methodologies and their extension to real practical problems while industry professionals will appreciate access to more advanced methodology than ever before.
Noble J, Adams NM, 2016, Correlation-based Streaming Anomaly Detection in Cyber-Security, 16th IEEE International Conference on Data Mining (ICDM), Publisher: IEEE, Pages: 311-318, ISSN: 2375-9232
Bakoben M, Adams N, Bellotti A, 2016, Uncertainty aware clustering for behaviour in enterprise networks, 16th IEEE International Conference on Data Mining (ICDM), Publisher: IEEE, Pages: 269-272, ISSN: 2375-9232
Plasse J, Adams N, 2016, Handling Delayed Labels in Temporally Evolving Data Streams, 4th IEEE International Conference on Big Data (Big Data), Publisher: IEEE, Pages: 2416-2424
Evangelou M, Adams NM, 2016, Predictability of NetFlow data, 14th IEEE International Conference on Intelligence and Security Informatics - Cybersecurity and Big Data (IEEE ISI), Publisher: IEEE, Pages: 67-72
Whitehouse M, Evangelou M, Adams NM, 2016, Activity-based temporal anomaly detection in enterprise-cyber security, 14th IEEE International Conference on Intelligence and Security Informatics - Cybersecurity and Big Data (IEEE ISI), Publisher: IEEE, Pages: 248-250
Rubin-Delanchy P, Adams NM, Heard NA, 2016, Disassortativity of Computer Networks, 14th IEEE International Conference on Intelligence and Security Informatics - Cybersecurity and Big Data (IEEE ISI), Publisher: IEEE, Pages: 243-247
Weston DJ, Russell RA, Batty E, et al., 2015, New quantitative approaches reveal the spatial preference of nuclear compartments in mammalian fibroblasts, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 12, ISSN: 1742-5689
Hand DJ, Adams NM, 2014, Selection bias in credit scorecard evaluation, JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, Vol: 65, Pages: 408-415, ISSN: 0160-5682
Adams N, Heard N, 2014, Data analysis for network cyber-security, ISBN: 9781783263745
© 2014 by Imperial College Press. There is increasing pressure to protect computer networks against unauthorized intrusion, and some work in this area is concerned with engineering systems that are robust to attack. However, no system can be made invulnerable. Data Analysis for Network Cyber-Security focuses on monitoring and analyzing network traffic data, with the intention of preventing, or quickly identifying, malicious activity. Such work involves the intersection of statistics, data mining and computer science. Fundamentally, network traffic is relational, embodying a link between devices. As such, graph analysis approaches are a natural candidate. However, such methods do not scale well to the demands of real problems, and the critical aspect of the timing of communications events is not accounted for in these approaches. This book gathers papers from leading researchers to provide both background to the problems and a description of cutting-edge methodology. The contributors are from diverse institutions and areas of expertise and were brought together at a workshop held at the University of Bristol in March 2013 to address the issues of network cyber security. The workshop was supported by the Heilbronn Institute for Mathematical Research.
Lawson DJ, Rubin-Delanchy P, Heard N, et al., 2014, Statistical frameworks for detecting tunnelling in cyber defence using big data, IEEE Joint Intelligence and Security Informatics Conference (JISIC 2014), Publisher: IEEE, Pages: 248-251
Bodenham DA, Adams NM, 2014, Adaptive change detection for relay-like behaviour, IEEE Joint Intelligence and Security Informatics Conference (JISIC 2014), Publisher: IEEE, Pages: 252-255
Adams NM, Lawson D, 2014, An approximate framework for flexible network flow screening, IEEE Joint Intelligence and Security Informatics Conference (JISIC 2014), Publisher: IEEE, Pages: 256-259
Rubin-Delanchy P, Lawson DJ, Turcotte MJ, et al., 2014, Three statistical approaches to sessionizing network flow data, IEEE Joint Intelligence and Security Informatics Conference (JISIC 2014), Publisher: IEEE, Pages: 244-247
Evans LPG, adams N, anagnostopoulos C, When Does Active Learning Work?, Advances in Intelligent Data Analysis XII, ISSN: 0302-9743
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