Geometric anomaly detection in data by Stolz, BJ, Tanner, J, Harrington, HA, Nanda, V

Anomaly detection for industrial big data by Caithness, N, Wallom, D

“…Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances.…”

An assessment of anomaly detection methods applied to microtunnelling by Sheil, B, Suryasentana, S, Cheng, W-C

“…The proliferation of data collected by modern tunnel boring machines presents a substantial opportunity for the application of data-driven anomaly detection (AD) techniques that can adapt dynamically to site specific conditions. …”

Anomaly detection using pattern-of-life visual metaphors by Happa, J, Bashford-Rogers, T, Agrafiotis, I, Goldsmith, M, Creese, S

“…Similar to other anomaly-detection techniques, false positives do exist in our general approach as well. …”

Anomaly detection and removal using non-stationary Gaussian processes by Reece, S, Garnett, R, Osborne, M, Roberts, S

Modeling Advanced Persistent Threats to enhance anomaly detection techniques by Atapour, C, Agrafiotis, I, Creese, S

“…We find that attributes from the Command and Control phase of these attacks provide unique features that can be used by any anomaly detection systems. We further validate how expressive our abstract models are by formalizing a fifth APT and examining the behavior that was not captured.…”

Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach by Srimokla, O, Pan-ngum, W, Khamsiriwatchara, A, Padungtod, C, Tipmontree, R, Choosri, N, Saralamba, S

“…In this study, an anomaly detection system is introduced as an early warning mechanism for potential malaria outbreaks in countries like Thailand.…”

Detecting smart meter false data attacks using hierarchical feature clustering and incentive weighted anomaly detection by Higgins, M, Stephen, B, Wallom, D

“…The authors examine an anomaly detection protocol for cyber-attacks that seek to leverage spot prices for financial gain. …”

Insider-threat detection using Gaussian mixture models and sensitivity profiles by Al Tabash, K, Happa, J

“…This paper introduces a novel automated anomaly detection method that uses Gaussian Mixture Models for modelling the normal behaviour of employees to detect anomalous behaviour that may be malicious. …”

First- to second-language reading comprehension: not transfer, but access by Walter, C

“…To examine whether the SBF predicts L2 readers’ performance, two groups of French learners of English performed an anomaly detection task. Results corresponded to the predictions of the SBF. …”

Prediction of microtunnelling jacking forces using a probabilistic observational approach by Sheil, B

“…The ability to predict the total jacking force requirements during a drive is highly desirable for anomaly detection, to ensure the available thrust is not exceeded, and to prevent damage to the pipe string and/or launch shaft. …”

In-network machine learning using programmable network devices: a survey by Zheng, C, Hong, X, Ding, D, Vargaftik, S, Ben-Itzhak, Y, Zilberman, N

“…Machine learning is widely used to solve networking challenges, ranging from traffic classification and anomaly detection to network configuration. However, machine learning also requires significant processing and often increases the load on both networks and servers. …”

Insider-threat detection: Lessons from deploying the CITD tool in three multinational organisations by Erola, A, Agrafiotis, I, Goldsmith, M, Creese, S

“…In order to fill this gap, we collaborated with three multinational commercial organisations who trialled our anomaly detection system, and worked with us to understand performance constraints for insider threat detection deployment and innate weaknesses in their operational contexts. …”

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme env... by Radanliev, P, De Roure, D, Page, K, Van Kleek, M, Santos, O, Maddox, L, Burnap, P, Anthi, E, Maple, C

“…The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. …”

Scores for multivariate distributions and level sets by Meng, X, Taylor, J, Ben Taieb, S, Li, S

“…In some multivariate contexts, it is a forecast of a level set that is needed, such as a density level set for anomaly detection or the level set of the cumulative distribution as a measure of risk. …”

A unique, ring-like radio source with quadrilateral structure detected with machine learning by Lochner, M, Rudnick, L, Heywood, I, Knowles, K, Shabala, SS

“…<p>We report the discovery of a unique object in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) using the machine learning anomaly detection framework ASTRONOMALY. This strange, ring-like source is 30′ from the MGCLS field centred on Abell 209, and is not readily explained by simple physical models. …”

A Bayesian approach to insider threat detection by Wall, A, Agrafiotis, I

“…The majority of proposed models for insider threat anomaly detection, mainly focus on processing network data. …”

A formalised approach to designing sonification systems for network-security monitoring by Axon, L, Nurse, J, Goldsmith, M, Creese, S

“…Security analysts working in SOCs generally monitor networks using a combination of anomaly-detection techniques, Intrusion Detection Systems and data presented in visual and text-based forms. …”

Distributed inference condition monitoring system for rural infrastructure in the developing world by Greeff, H, Manandhar, A, Thomson, P, Hope, R, Clifton, D

“…This novel method has proven that it is possible for rural operating, resource-constrained devices to use lightweight, on-board machine learning approaches to perform anomaly detection in the embedded system. Distributed inference between the embedded system at the rural node and powerful cloud-based machine learning algorithms offer robust information without the need for expensive hardware or sensors embedded in situ – making the possibility of a large-scale (and perhaps even continent-wide) monitoring system feasible.…”

Local2Global: a distributed approach for scaling representation learning on graphs by Jeub, LGS, Colavizza, G, Dong, X, Bazzi, M, Cucuringu, M

“…We also consider the downstream task of anomaly detection and show how one can use <i>local2global</i> to highlight anomalies in cybersecurity networks.…”