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Relevant bibliographies by topics / Contextual anomalies

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Journal articles
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Book chapters
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Academic literature on the topic 'Contextual anomalies'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Contextual anomalies"

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Yu, Xiang, Hui Lu, Xianfei Yang, Ying Chen, Haifeng Song, Jianhua Li, and Wei Shi. "An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772092047. http://dx.doi.org/10.1177/1550147720920478.

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With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.

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Filik, Ruth. "Contextual override of pragmatic anomalies: Evidence from eye movements." Cognition 106, no. 2 (February 2008): 1038–46. http://dx.doi.org/10.1016/j.cognition.2007.04.006.

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Ngueilbaye, Alladoumbaye, Hongzhi Wang, Daouda Ahmat Mahamat, Ibrahim A. Elgendy, and Sahalu B. Junaidu. "Methods for detecting and correcting contextual data quality problems." Intelligent Data Analysis 25, no. 4 (July 9, 2021): 763–87. http://dx.doi.org/10.3233/ida-205282.

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Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).

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Clausen, Henry, Gudmund Grov, and David Aspinall. "CBAM: A Contextual Model for Network Anomaly Detection." Computers 10, no. 6 (June 11, 2021): 79. http://dx.doi.org/10.3390/computers10060079.

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Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a contextual bidirectional anomaly model (CBAM) based on deep LSTM-networks that is specifically designed to detect such attacks as contextual network anomalies. The model efficiently learns short-term sequential patterns in network flows as conditional event probabilities. Access attacks frequently break these patterns when exploiting vulnerabilities, and can thus be detected as contextual anomalies. We evaluated CBAM on an assembly of three datasets that provide both representative network access attacks, real-life traffic over a long timespan, and traffic from a real-world red-team attack. We contend that this assembly is closer to a potential deployment environment than current NIDS benchmark datasets. We show that, by building a deep model, we are able to reduce the false positive rate to 0.16% while effectively detecting six out of seven access attacks, which is significantly lower than the operational range of other methods. We further demonstrate that short-term flow structures remain stable over long periods of time, making the CBAM robust against concept drift.

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Seymour, Deni J. "Contextual Incongruities, Statistical Outliers, and Anomalies: Targeting Inconspicuous Occupational Events." American Antiquity 75, no. 1 (January 2010): 158–76. http://dx.doi.org/10.7183/0002-7316.75.1.158.

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New methodologies are needed to address multiple componentcy and short reuse episodes that are characteristic of mobile group residential and logistical strategies. Chronometric results are often misinterpreted when evaluated within a framework suited to long-term sedentary occupations. The standard practices of age-averaging, eliminating apparent "anomalous" results, and relying on high profile diagnostic tools and vessels and the most visible features—along with the expectation for "contextual congruence"—mask multi-componentcy and episodic reuse. High incidences of site reuse have been detected by considering alternate site development models and looking specifically for evidence of distinct shorter term occupations.

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Dou, Shaoyu, Kai Yang, and H. Vincent Poor. "PC2A: Predicting Collective Contextual Anomalies via LSTM With Deep Generative Model." IEEE Internet of Things Journal 6, no. 6 (December 2019): 9645–55. http://dx.doi.org/10.1109/jiot.2019.2930202.

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Wei, Ji Dong, and Ge Guo. "Multi-Sensor Stockline Tracking within a Blast Furnace." Applied Mechanics and Materials 701-702 (December 2014): 522–27. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.522.

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The paper presents a synergistic approach for height tracking within a blast furnace (BF). The Frequency Modulated Continuous Wave (FMCW) radar has been employed to measure the height and surface profile of the burden surface. However the radar signal is easily disturbed, by the radar anomalies, during the process of continuous measurement. The data from rotating chute and charging switch provide information on contextual relevance with radar anomalies. An anomaly detection models has been developed to increase the measurement accuracy by utilizing contextual information. The approach has been validated on real BF. The root mean squared (RMS) error in the measured height is reduced by 17% when using the proposed approach compared to the case without it. The results suggest that the proposed approach successfully adapts to changes in the pattern and characteristics of the burden surface.

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Al-Gabalawy, Mostafa. "Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency." Applied Soft Computing 96 (November 2020): 106715. http://dx.doi.org/10.1016/j.asoc.2020.106715.

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Frolova, E. A. "Typology of speech anomalies in the present-day advertising language." Russian language at school 84, no. 3 (May 22, 2023): 68–76. http://dx.doi.org/10.30515/0131-6141-2023-84-3-68-76.

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The article considers different groups of speech anomalies found in modern advertising texts. The aim is threefold: to comprehensively analysis speech deviations in advertising texts, to develop a typology of speech anomalies and to determine their role in ensuring the communicative effectiveness of an advertising message. The study used analy sis (lexical, componential, word-formation, classification and differentiation), comparative and integrative methods, as well as the contextual-semantic analysis technique. These procedures enable identification of the content and semantic load of advertising text components. Drawing on relevant scientific literature, I communicate my own vision of the typology of speech anomalies. The paper explains a rationale for considering the inter-step word-formation as the basis for creating speech anomalies and the interaction of elements of different language levels as a condition for the appearance of deviations.

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Zhao, Bo, Xiang Li, Jiayue Li, Jianwen Zou, and Yifan Liu. "An Area-Context-Based Credibility Detection for Big Data in IoT." Mobile Information Systems 2020 (January 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5068731.

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In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

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Dissertations / Theses on the topic "Contextual anomalies"

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Görnitz, Nico Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Manfred [Gutachter] Opper, and Marius [Gutachter] Kloft. "One-class classification in the presence of point, collective, and contextual anomalies / Nico Görnitz ; Gutachter: Klaus-Robert Müller, Manfred Opper, Marius Kloft ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2019. http://d-nb.info/1178524663/34.

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Görnitz, Nico [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Manfred [Gutachter] Opper, and Marius [Gutachter] Kloft. "One-class classification in the presence of point, collective, and contextual anomalies / Nico Görnitz ; Gutachter: Klaus-Robert Müller, Manfred Opper, Marius Kloft ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2019. http://d-nb.info/1178524663/34.

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Wilmet, Audrey. "Détection d'anomalies dans les flots de liens : combiner les caractéristiques structurelles et temporelles." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS402.

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Un flot de liens est un ensemble de liens {(t,u,v)} dans lequel un triplet (t,u,v) modélise l'interaction entre deux entités u et v à l'instant t. Dans de nombreuses situations, les données résultent de la mesure des interactions entre plusieurs millions d'entités au cours du temps et peuvent ainsi être étudiées grâce au formalisme des flots de liens. C'est le cas des appels téléphoniques, des échanges d'e-mails, des transferts d'argent, des contacts entre individus, du trafic IP, des achats en ligne, et bien d'autres encore. L'objectif de cette thèse est la détection d'ensembles de liens anormaux dans un flot de liens. Dans une première partie, nous concevons une méthode qui construit différents contextes, un contexte étant un ensemble de caractéristiques décrivant les circonstances d'une anomalie. Ces contextes nous permettent de trouver des comportements inattendus pertinents, selon plusieurs dimensions et perspectives. Dans une seconde partie, nous concevons une méthode permettant de détecter des anomalies dans des distributions hétérogènes dont le comportement est constant au cours du temps, en comparant une séquence de distributions hétérogènes similaires. Nous appliquons nos outils méthodologiques à des interactions temporelles provenant de retweets sur Twitter et de trafic IP du groupe MAWI
A link stream is a set of links {(t, u, v)} in which a triplet (t, u, v) models the interaction between two entities u and v at time t. In many situations, data result from the measurement of interactions between several million of entities over time and can thus be studied through the link stream's formalism. This is the case, for instance, of phone calls, email exchanges, money transfers, contacts between individuals, IP traffic, online shopping, and many more. The goal of this thesis is the detection of sets of abnormal links in a link stream. In a first part, we design a method that constructs different contexts, a context being a set of characteristics describing the circumstances of an anomaly. These contexts allow us to find unexpected behaviors that are relevant, according to several dimensions and perspectives. In a second part, we design a method to detect anomalies in heterogeneous distributions whose behavior is constant over time, by comparing a sequence of similar heterogeneous distributions. We apply our methodological tools to temporal interactions coming from retweets of Twitter and IP traffic of MAWI group

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Vasco, Daniela Oliveira Baía Soares. "Identificação de Anomalias Contextuais." Master's thesis, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.

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Vasco, Daniela Oliveira Baía Soares. "Identificação de Anomalias Contextuais." Dissertação, 2013. https://repositorio-aberto.up.pt/handle/10216/70366.

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Book chapters on the topic "Contextual anomalies"

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Vaudaine, Rémi, Baptiste Jeudy, and Christine Largeron. "Detection of Contextual Anomalies in Attributed Graphs." In Advances in Intelligent Data Analysis XIX, 338–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74251-5_27.

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Corizzo, Roberto, Michelangelo Ceci, Gianvito Pio, Paolo Mignone, and Nathalie Japkowicz. "Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data." In Discovery Science, 461–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88942-5_36.

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Baldoni, Sara, Giuseppe Celozzi, Alessandro Neri, Marco Carli, and Federica Battisti. "Inferring Anomaly Situation from Multiple Data Sources in Cyber Physical Systems." In Cyber-Physical Security for Critical Infrastructures Protection, 67–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69781-5_5.

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AbstractCyber physical systems are becoming ubiquitous devices in many fields thus creating the need for effective security measures. We propose to exploit their intrinsic dependency on the environment in which they are deployed to detect and mitigate anomalies. To do so, sensor measurements, network metrics, and contextual information are fused in a unified security architecture. In this paper, the model of the proposed framework is presented and a first proof of concept involving a telecommunication infrastructure case study is provided.

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Khrennikov, Andrei. "Contextual Approach to Quantum Theory." In Information Dynamics in Cognitive, Psychological, Social and Anomalous Phenomena, 153–85. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0479-3_9.

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Santhi, M., and Leya Elizabeth Sunny. "Contextual Multi-scale Region Convolutional 3D Network for Anomalous Activity Detection in Videos." In Computational Vision and Bio-Inspired Computing, 98–108. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_12.

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Zaverucha, Gerson. "A prioritized Contextual Default Logic: Curing anomalous extensions with a simple abnormality default theory." In KI-94: Advances in Artificial Intelligence, 260–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58467-6_23.

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Wolff, Christian. "Composing Aseneth." In When Aseneth Met Joseph, 19–49. Oxford University PressNew York, NY, 1998. http://dx.doi.org/10.1093/oso/9780195114751.003.0003.

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Abstract Ancient readers of Jewish scripture often found themselves confronted by textual anomalies, which they attempted to resolve. The means by which at least one circle of Jewish readers frequently did so is brilliantly illuminated in Kugel’s study of midrashic rabbinic traditions about Joseph and the wife of Potiphar, where he demonstrates their formulation and development as logical responses to textual and contextual anomalies and questions.

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Zadeh, Esmaeil, Stephen Amstutz, James Collins, Craig Ingham, Marian Gheorghe, and Savas Konur. "Automated Contextual Anomaly Detection for Network Interface Bandwidth Utilisation: A Case Study in Network Capacity Management." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210459.

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We present a contextual anomaly detection methodology utilised for the capacity management process of a managed service provider that administers networks for large enterprises. We employ an ensemble of forecasts to identify anomalous network traffic. Stream of observations, upon their arrival, are compared against these baseline forecasts and alerts generated only if the anomalies are sustained. The results confirm that our approach significantly reduces false alerts, triggering rather more accurate and meaningful alerts to a level that could be proactively consumed by a small team. We believe our methodology makes a useful contribution to the applications enabling proactive capacity management.

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Dhibar, Kunal, and Prasenjit Maji. "Future Outlier Detection Algorithm for Smarter Industry Application Using ML and AI." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 152–66. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8785-3.ch008.

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Throughout many real-world investigations, outliers are prevalent. Even a few aberrant data points can cause modeling misspecification, biased parameter estimate, and poor forecasting. Outliers in a time series are typically created at unknown moments in time by dynamic intervention models. As a result, recognizing outliers is the starting point for every statistical investigation. Outlier detection has attracted significant attention in a variety of domains, most notably machine learning and artificial intelligence. Anomalies are classified as strong outliers into point, contextual, and collective outliers. The most significant difficulties in outlier detection include the narrow boundary between remote sites and natural areas, the propensity of fresh data and noise to resemble genuine data, unlabeled datasets, and varying interpretations of outliers in different applications.

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Palakurti, Naga Ramesh. "Challenges and Future Directions in Anomaly Detection." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 269–84. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2909-2.ch020.

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Anomaly detection plays a critical role in various domains, including cybersecurity, finance, healthcare, and industrial monitoring by identifying unusual patterns or events that deviate from normal behavior. This chapter examines the challenges and future directions in anomaly detection, focusing on innovative techniques, emerging trends, and practical applications. Key challenges include the detection of subtle and evolving anomalies in large-scale, high-dimensional data streams, the integration of contextual information and domain knowledge for improved detection accuracy, and the mitigation of false positives and false negatives. Future directions encompass advancements in machine learning algorithms, such as deep learning and reinforcement learning, for enhanced anomaly detection performance, the integration of heterogeneous data sources and multi-modal information for comprehensive anomaly assessment, and the development of adaptive and self-learning anomaly detection systems capable of adapting to dynamic environments and evolving threats.

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Conference papers on the topic "Contextual anomalies"

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Vasco, Daniela, Pedro Pereira Rodrigues, and Joao Gama. "Contextual anomalies in medical data." In 2013 IEEE 26th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2013. http://dx.doi.org/10.1109/cbms.2013.6627869.

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Prado-Romero, Mario Alfonso, and Andres Gago-Alonso. "Detecting contextual collective anomalies at a Glance." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900017.

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Dimopoulos, Giorgos, Pere Barlet-Ros, Constantine Dovrolis, and Ilias Leontiadis. "Detecting network performance anomalies with contextual anomaly detection." In 2017 IEEE International Workshop on Measurements & Networking (M&N). IEEE, 2017. http://dx.doi.org/10.1109/iwmn.2017.8078404.

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Latif, Hamid, José Suárez-Varela, Albert Cabellos-Aparicio, and Pere Barlet-Ros. "Detecting Contextual Network Anomalies with Graph Neural Networks." In CoNEXT 2023: The 19th International Conference on emerging Networking EXperiments and Technologies. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3630049.3630171.

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Carmona, Chris U., François-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus. "Neural Contextual Anomaly Detection for Time Series." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/394.

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We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed for computer vision that we tailor to the time series setting. Our window-based approach facilitates learning the boundary between normal and anomalous classes by injecting generic synthetic anomalies into the available data. NCAD can effectively take advantage of domain knowledge and of any available training labels. We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance in the supervised, semi-supervised, and unsupervised settings.

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Fan Jiang, Ying Wu, and Aggelos K. Katsaggelos. "Detecting contextual anomalies of crowd motion in surveillance video." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414535.

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Sharma, Shivam, Mirdul Swarup, Tanush Mahajan, and Zeel Dilipkumar Patel. "Detecting anomalies, contradictions, and contextual analysis through NLP in text." In 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2022. http://dx.doi.org/10.1109/icict55121.2022.10064560.

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Jinadasa, Minura, Suranga Nisiwasala, Suthan Senthinathan, Shiromi Arunatileka, and Damitha Sandaruwan. "Framework for detection of anomalies in mass moving objects by non-technical users utilizing contextual & spatio-temporal data." In 2017 Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2017. http://dx.doi.org/10.1109/icter.2017.8257798.

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Schaefer, S., and O. Revheim. "Building Trust in AI/ML Solutions: Key Factors for Successful Adoption in Drilling Optimization and Hazard Prevention." In SPE Norway Subsurface Conference. SPE, 2024. http://dx.doi.org/10.2118/218455-ms.

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The use of AI/ML technologies has provided breakthrough performance in automated predictive data analytics. With the increasing amount of data available during drilling operations, data driven AI/ML solutions lay out the future of current technologies for drilling optimization and hazard prevention. Fast adoption and appeal of these technologies to the industry could be explained by a few reasons: AI/ML enables digital transformation by using only real-time data without extensive requirements for contextual data so that engineering and data input processes can be fully automated;AI/ML solutions predict outputs based on the data trends allowing to solve problems where conventional models are hard to implement or are not sensitive enough to identify subtle anomalies;Targeted solutions address specific problems and become more applicable in the modern digital ecosystem,Due to previous reasons, such technologies are easier to implement and to scale up in the operational environment. Successful adoption of AI/ML technologies lies in its validation and trust in the operational environment. Based on the project experience from various parts of the world, prerequisites for building trust have proven to be: high performance AL/ML technology;matured IT infrastructure with relevant support services to enable digital transformation;monitoring specialists in an established RTOC or rigsite team to validate solution decisions;good communication protocol and established responsibilities of the RTOC and rig team to validate the impact of the predictions and to apply for operations. The success factors are consequently related to technology, infrastructure and "soft" aspects like work processes, team interactions and defined roles and responsibilities. Each of these areas will be addressed individually.

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Sun, Kewen, Chao Mu, Tao Yu, and Graeme Paterson. "An Innovative Workflow for Real-Time Torque and Drag Monitoring." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212535-ms.

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Abstract Abnormal torque and drag (T & D), which commonly includes overpull, underpull, and high-torque load, are indications of excess frictional effects between the drillstring and the wellbore walls. Numerous conditions can cause these effects, including tight hole, differential sticking, poor hole cleaning, key seats, etc. Failure to observe these anomalies will cause excessive wear on the drillstring and can eventually lead to severe stuck pipe conditions. A new workflow for monitoring T & D is presented in this paper. This workflow, developed for a real-time monitoring system, allows for monitoring various types of data from multiple sources to be received without delay, aligned, and synchronized. The workflow requires standard surface measurements and contextual data, which are available on most wells. Three main segments with respect to the computation phase are included in the workflow. These segments include T & D measurement points statistics, T & D modelling and calibration, and abnormal T & D alarms. The measurement points are selected from relevant operations and summarize the statistics at different granularities to meet the different objectives, such as the classical broomstick plot or alarm triggering. A hybrid T & D modelling framework was designed to predict the hook load and surface torque accordingly. This framework combines the mathematical capability of a stiff-string model using a finite element method and the experience acquired from obtaining the drilling data. As a result, the physical model can be automatically calibrated and driven by real-time data to compensate the hook load offset due to uncertain variables or inaccurate inputs. An alarm-triggering logic can be developed to capture anomalies based on a comparison between the measured and predicted values. The new workflow is fully automatic without a need for manual calibration and fixed thresholds. Furthermore, the workflow adjusts itself according to real-time observations, which makes it adaptive to the changing conditions of the well being drilled. The efficiency and reliability of the anomaly detection heavily rely on the input data quality in the perspective of stream computation. In this paper, two case studies are presented containing the results produced by streaming actual well data in a time series manner. The case studies demonstrate the usability and reasonableness obtained by the user when handling the actual operation scenarios. The work presented in this paper was developed to meet the increase in digital transformation by the oil and gas industry and demonstrates the best use of data for drilling optimization.

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