Relevant bibliographies by topics / Networks anomalies detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Networks anomalies detection'

Author: Grafiati
Published: 19 October 2024
Last updated: 31 July 2025

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Journal articles on the topic "Networks anomalies detection"

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Liao, Xiao Ju, Yi Wang, and Hai Lu. "Rule Anomalies Detection in Firewalls." Key Engineering Materials 474-476 (April 2011): 822–27. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.822.

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Firewall is the most prevalent and important technique to enforce the security inside the networks. However, effective and free anomalies rules management in large and fast growing networks becomes increasingly challenging. In this paper, we use a directed tree-based method to detect rule anomalies in firewall; in addition, this method can track the source of the anomalies. We believe the posed information will simplify the rules management and minimizing the networking vulnerability due to firewall rules misconfigurations.
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Rejito, Juli, Deris Stiawan, Ahmed Alshaflut, and Rahmat Budiarto. "Machine learning-based anomaly detection for smart home networks under adversarial attack." Computer Science and Information Technologies 5, no. 2 (2024): 122–29. http://dx.doi.org/10.11591/csit.v5i2.pp122-129.

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As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detect
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Rejito, Juli, Deris Stiawan, Ahmed Alshaflut, and Rahmat Budiarto. "Machine learning-based anomaly detection for smart home networks under adversarial attack." Computer Science and Information Technologies 5, no. 2 (2024): 122–29. http://dx.doi.org/10.11591/csit.v5i2.p122-129.

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As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detect
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Juli, Rejito, Stiawan Deris, Alshaflut Ahmed, and Budiarto Rahmat. "Machine learning-based anomaly detection for smart home networks under adversarial attack." Computer Science and Information Technologies 5, no. 2 (2024): 122–29. https://doi.org/10.11591/csit.v5i2.pp122-129.

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As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detect
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Navale, Manisha Pandurang, and Brijendra P. Gupta. "DEEP LEARNING ALGORITHMS FOR DETECTION AND CLASSIFICATION OF CONGENITAL BRAIN ANOMALY." ICTACT Journal on Image and Video Processing 13, no. 4 (2023): 2995–3001. http://dx.doi.org/10.21917/ijivp.2023.0426.

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Congenital brain anomalies are structural abnormalities that occur during fetal development and can have a significant impact on an individual neurological function. Detecting and classifying these anomalies accurately and efficiently is crucial for early diagnosis, intervention, and treatment planning. In recent years, recurrent neural networks (RNNs) have emerged as powerful tools for analyzing sequential and time-series data in various domains, including medical imaging. This research presents an overview of RNN-based algorithms for the detection and classification of congenital brain anoma
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Kumar D,, Ravi. "Anomaly Detection in Networks." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47520.

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-In Network Security is a major challenge in the digital world. Intrusion is common in many applications and intruders are sophisticated enough to change their attack pattern very often. To address this issue, the development of a model for the detection of network anomalies and intrusions. The approach utilizes the Borderline Synthetic Minority Over-Sampling Technique (SMOTE) along with Support Vector Machines (SVM) to enhance anomaly detection
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Alfardus, Asma, and Danda B. Rawat. "Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks." Electronics 13, no. 10 (2024): 1962. http://dx.doi.org/10.3390/electronics13101962.

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In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering
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Gonela Kavya Pavani, Bobba Veeramallu. "Hybrid Machine Learning Framework for Anomaly Detection in 5G Networks." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 733–39. https://doi.org/10.52783/jisem.v10i32s.5406.

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The rapid adoption of 5G networks has transformed the communication landscape, offering unprecedented speed, capacity, and connectivity for diverse applications such as IoT, autonomous vehicles, and critical infrastructure. However, this evolution also introduces vulnerabilities that can compromise network performance, security, and reliability. Anomaly detection, the process of identifying irregular patterns or deviations in network traffic, has emerged as a critical mechanism to ensure the resilience of 5G networks. It enables proactive identification of issues such as latency spikes, packet
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Mažeika, Dalius, and Saulius Jasonis. "NETWORK TRAFFIC ANOMALIES DETECTING USING MAXIMUM ENTROPY METHOD / KOMPIUTERIŲ TINKLO SRAUTO ANOMALIJŲ ATPAŽINIMAS MAKSIMALIOS ENTROPIJOS METODU." Mokslas – Lietuvos ateitis 6, no. 2 (2014): 162–67. http://dx.doi.org/10.3846/mla.2014.22.

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The problem of traffic anomalies in computer networks is analyzed. NetFlow packets are used as network traffic data and maximum entropy methods is used for anomalies detection. Method detects network anomalies by comparing the current network traffic against a baseline distribution. Method is adopted according to NetFow data and performace of the method is improved. Prototype of anomalies detection system was developed and experimental investigation carried out. Results of investigation confirmed that method is sensitive to deviations of the network traffic and can be successfully used for net
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Rizwan, Ramsha, Farrukh Aslam Khan, Haider Abbas, and Sajjad Hussain Chauhdary. "Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism." International Journal of Distributed Sensor Networks 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/684952.

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During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS
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Dissertations / Theses on the topic "Networks anomalies detection"

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Sithirasenan, Elankayer. "Substantiating Anomalies in Wireless Networks Using Outlier Detection Techniques." Thesis, Griffith University, 2009. http://hdl.handle.net/10072/365690.

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With the increasing dependence on Wireless Local Area Networks (WLANs), businesses and educational institutions are in real need of a robust security mechanism. The latest WLAN security protocol, the IEEE 802.11i assures rigid security for wireless networks with the support of IEEE 802.1x protocol for authentication, authorization and key distribution. Nevertheless, users remain skeptical since they lack confidence on the practical trustworthiness of these security mechanisms. In this research we propose a novel Early Warning System (EWS), built on the foundations of IEEE 802.11i security arch
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Abuaitah, Giovani Rimon. "ANOMALIES IN SENSOR NETWORK DEPLOYMENTS: ANALYSIS, MODELING, AND DETECTION." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1376594068.

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Verner, Alexander. "LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1074.

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In order to ensure the validity of sensor data, it must be thoroughly analyzed for various types of anomalies. Traditional machine learning methods of anomaly detections in sensor data are based on domain-specific feature engineering. A typical approach is to use domain knowledge to analyze sensor data and manually create statistics-based features, which are then used to train the machine learning models to detect and classify the anomalies. Although this methodology is used in practice, it has a significant drawback due to the fact that feature extraction is usually labor intensive and requir
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Kamat, Sai Shyamsunder. "Analyzing Radial Basis Function Neural Networks for predicting anomalies in Intrusion Detection Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259187.

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In the 21st century, information is the new currency. With the omnipresence of devices connected to the internet, humanity can instantly avail any information. However, there are certain are cybercrime groups which steal the information. An Intrusion Detection System (IDS) monitors a network for suspicious activities and alerts its owner about an undesired intrusion. These commercial IDS’es react after detecting intrusion attempts. With the cyber attacks becoming increasingly complex, it is expensive to wait for the attacks to happen and respond later. It is crucial for network owners to emplo
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Kabore, Raogo. "Hybrid deep neural network anomaly detection system for SCADA networks." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2020. http://www.theses.fr/2020IMTA0190.

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Les systèmes SCADA sont de plus en plus ciblés par les cyberattaques en raison de nombreuses vulnérabilités dans le matériel, les logiciels, les protocoles et la pile de communication. Ces systèmes utilisent aujourd'hui du matériel, des logiciels, des systèmes d'exploitation et des protocoles standard. De plus, les systèmes SCADA qui étaient auparavant isolés sont désormais interconnectés aux réseaux d'entreprise et à Internet, élargissant ainsi la surface d'attaque. Dans cette thèse, nous utilisons une approche deep learning pour proposer un réseau de neurones profonds hybride efficace pour l
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Jin, Fang. "Algorithms for Modeling Mass Movements and their Adoption in Social Networks." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72292.

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Online social networks have become a staging ground for many modern movements, with the Arab Spring being the most prominent example. In an effort to understand and predict those movements, social media can be regarded as a valuable social sensor for disclosing underlying behaviors and patterns. To fully understand mass movement information propagation patterns in social networks, several problems need to be considered and addressed. Specifically, modeling mass movements that incorporate multiple spaces, a dynamic network structure, and misinformation propagation, can be exceptionally useful i
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Mdini, Maha. "Anomaly detection and root cause diagnosis in cellular networks." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0144/document.

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Grâce à l'évolution des outils d'automatisation et d'intelligence artificielle, les réseauxmobiles sont devenus de plus en plus dépendants de la machine. De nos jours, une grandepartie des tâches de gestion de réseaux est exécutée d'une façon autonome, sans interventionhumaine. Dans cette thèse, nous avons focalisé sur l'utilisation des techniques d'analyse dedonnées dans le but d'automatiser et de consolider le processus de résolution de défaillancesdans les réseaux. Pour ce faire, nous avons défini deux objectifs principaux : la détectiond'anomalies et le diagnostic des causes racines de ces
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Moussa, Mohamed Ali. "Data gathering and anomaly detection in wireless sensors networks." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1082/document.

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L'utilisation des réseaux de capteurs sans fil (WSN) ne cesse d'augmenter au point de couvrir divers domaines et applications. Cette tendance est supportée par les avancements techniques achevés dans la conception des capteurs, qui ont permis de réduire le coût ainsi que la taille de ces composants. Toutefois, il reste plusieurs défis qui font face au déploiement et au bon fonctionnement de ce type de réseaux et qui parviennent principalement de la limitation des ressources de capteurs ainsi de l'imperfection des données collectées. Dans cette thèse, on adresse le problème de collecte de donné
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Ky, Joël Roman. "Anomaly Detection and Root Cause Diagnosis for Low-Latency Applications in Time-Varying Capacity Networks." Electronic Thesis or Diss., Université de Lorraine, 2025. http://www.theses.fr/2025LORR0026.

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L'évolution des réseaux a conduit à l'émergence d'applications à faible latence (FL) telles que le cloud gaming (CG) et la réalité virtuelle basée sur le cloud (Cloud VR), qui exigent des conditions réseau strictes, notamment une faible latence et une bande passante élevée. Cependant, les réseaux à capacité variable introduisent des dégradations, telles que du délai, des fluctuations de bande passante et des pertes de paquets, qui peuvent significativement altérer l'expérience utilisateur sur les applications FL. Cette thèse vise à concevoir des méthodologies pour détecter et diagnostiquer les
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Audibert, Julien. "Unsupervised anomaly detection in time-series." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS358.

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La détection d'anomalies dans les séries temporelles multivariées est un enjeu majeur dans de nombreux domaines. La complexité croissante des systèmes et l'explosion de la quantité de données ont rendu son automatisation indispensable. Cette thèse propose une méthode non supervisée de détection d'anomalies dans les séries temporelles multivariées appelée USAD. Cependant, les méthodes de réseaux de neurones profonds souffrent d'une limitation dans leur capacité à extraire des caractéristiques des données puisqu'elles ne s'appuient que sur des informations locales. Afin d'améliorer les performan
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Books on the topic "Networks anomalies detection"

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T, Feagin, Overland D, University of Houston--Clear Lake. Research Institute for Computing and Information Systems., and Lyndon B. Johnson Space Center., eds. Communications and tracking expert systems study. Research Institute for Computing and Information Systems, University of Houston--Clear Lake, 1987.

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Parisi, Alessandro. Hands-On Artificial Intelligence for Cybersecurity: Implement Smart AI Systems for Preventing Cyber Attacks and Detecting Threats and Network Anomalies. Packt Publishing, Limited, 2019.

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Hands-On Artificial Intelligence for Cybersecurity: Implement Smart AI Systems for Preventing Cyber Attacks and Detecting Threats and Network Anomalies. de Gruyter GmbH, Walter, 2019.

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Book chapters on the topic "Networks anomalies detection"

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Krzysztoń, Mateusz, Marcin Lew, and Michał Marks. "NAD: Machine Learning Based Component for Unknown Attack Detection in Network Traffic." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_4.

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AbstractDetection of unknown attacks is challenging due to the lack of exemplary attack vectors. However, previously unknown attacks are a significant danger for systems due to a lack of tools for protecting systems against them, especially in fast-evolving Internet of Things (IoT) technology. The most widely used approach for malicious behaviour of the monitored system is detecting anomalies. The vicious behaviour might result from an attack (both known and unknown) or accidental breakdown. We present a Net Anomaly Detector (NAD) system that uses one-class classification Machine Learning tech
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Akashi, Osamu, Atsushi Terauchi, Kensuke Fukuda, Toshio Hirotsu, Mitsuru Maruyama, and Toshiharu Sugawara. "Detection and Diagnosis of Inter-AS Routing Anomalies by Cooperative Intelligent Agents." In Ambient Networks. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11568285_16.

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Čermák, Milan, Pavel Čeleda, and Jan Vykopal. "Detection of DNS Traffic Anomalies in Large Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13488-8_20.

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Doshi, Vyom, Danish Sheikh, Nishtha Sharma, and Pramod Bide. "Behavioral Anomalies Detection via Human Pose Estimation: A Study on Cheating Detection." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2179-8_26.

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Dawoud, Ahmed, Seyed Shahristani, and Chun Raun. "Unsupervised Deep Learning for Software Defined Networks Anomalies Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59540-4_9.

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Hossain, Md Azam, Iqram Hussain, Baseem Al-Athwari, and Santosh Dahit. "Network Traffic Anomalies Detection Using Machine Learning Algorithm: A Performance Study." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9480-6_26.

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Fañez, Mirko, Enrique A. de la Cal, Javier Sedano, Juan Luis Carús Candas, and Jairo Ramírez Ávila. "Human Acoustic Events Detection as Anomalies in Industrial Environments Using Shallow Unsupervised Techniques." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75013-7_10.

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Bhattacharya, Saurabh, and Manju Pandey. "Anomalies Detection on Contemporary Industrial Internet of Things Data for Securing Crucial Devices." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9228-5_2.

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LaRock, Timothy, Vahan Nanumyan, Ingo Scholtes, Giona Casiraghi, Tina Eliassi-Rad, and Frank Schweitzer. "HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks." In Proceedings of the 2020 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.52.

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Wankhade, Kapil Keshao, Snehlata Dongre, Ravi Chandra, Kishore V. Krishnan, and Srikanth Arasavilli. "Machine Learning-Based Detection of Attacks and Anomalies in Industrial Internet of Things (IIoT) Networks." In Applied Soft Computing and Communication Networks. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2004-0_7.

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Conference papers on the topic "Networks anomalies detection"

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Mukunthan, M. A., Jose Anand A., Stalin Kesavan, Malini Mutyala, R. Kesavan, and R. Geetha. "Detection of Physical Anomalies in IoT Networks Using Machine Learning Techniques." In 2024 International Conference on Advancement in Renewable Energy and Intelligent Systems (AREIS). IEEE, 2024. https://doi.org/10.1109/areis62559.2024.10893610.

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Ramantanis, Petros, Sébastien Bigo, Fabien Boitier, et al. "Detection of Fiber Macro-Bending Anomalous Events in Operator Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w2a.30.

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We experimentally measure the specific spectral signature of strong fiber bending and propose a metric to detect strong bending events. Leveraging telemetry from a production network we report short-lived anomalies which exhibit the aforementioned signature.
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Thakre, N. K., M. Misba, Sri Lavanya Sajja, Shyam K. Fardale, Veera Ankalu Vuyyuru, and M. K. Mohamed Faizal. "Unveiling Market Anomalies: Harnessing Convolutional Neural Networks for Fraud Detection in Finance." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10931899.

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Ture, Omkar D., Adbelkhan A. Pathan, Varun R. Kawade, Prasad N. Patil, Nutan V. Bansode, and Sonali Y. Sawant. "Early Detection of Fetal Skull Anomalies using Web-Based Convolutional Neural Networks." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774903.

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Huang, Hao, Tapan Shah, John Karigiannis, and Scott Evans. "Deep Root Cause Analysis: Unveiling Anomalies and Enhancing Fault Detection in Industrial Time Series." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650906.

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Gupta, Sachin, Bhoomi Gupta, and Babita Yadav. "Implementation of AI-Driven Intrusion Detection Systems to Analyze Anomalies and Network Traffic Patterns in Healthcare Networks." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070588.

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Cigiri, Rashmi, Gotte Ranjith Kumar, Haider Alabdeli, Y. M. Mahaboob John, and S. Kaliappan. "Harris Corner Detection Algorithm based Long Short-Term Memory for Detecting Distributed Denial of Service Anomalies in Software Defined Networks." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10941199.

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A, Padmavathi, Muntather Muhsin Hassan, Jashanpreet Singh, F. Anitha Florence Vinola, and N. Naga Saranya. "Securing Blockchain based Supply Chain in Agriculture using Isolated Forests with Local Outlier Factor for Anomalies Detection." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872393.

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Kannadasan, Tamilarasan. "Twin Support Vector Machine with Minkowski Gaussian Kernel Based Performance Anomalies Detection in Software Application During Runtime." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872212.

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Kolodziej, Joanna, Mateusz Krzyszton, and Pawel Szynkiewicz. "Anomaly Detection In TCP/IP Networks." In 37th ECMS International Conference on Modelling and Simulation. ECMS, 2023. http://dx.doi.org/10.7148/2023-0542.

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Intrusion Detection Systems (IDS) should be capable of quickly detecting attacks and network traffic anomalies to reduce the damage to the network components. They may efficiently detect threats based on prior knowledge of attack characteristics and the potential threat impact ('known attacks'). However, IDS cannot recognize threats, and attacks ('unknown attacks') usually occur when using brand-new technologies for system damage. This paper presents two security services -- Net Anomaly Detector (NAD) and a signature-based PGA Filter for detecting attacks and anomalies in TCP/IP networks. Both
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Reports on the topic "Networks anomalies detection"

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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a suffi
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f
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León, Carlos. Detecting anomalous payments networks: A dimensionality reduction approach. Banco de la República de Colombia, 2019. http://dx.doi.org/10.32468/be.1098.

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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the cor
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5

Valdez, Luis, and Alexander Heifetz. Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1827413.

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6

Rinehart, Aaron, M. Gregory, and Wendy Wright. Fixed-station water-quality monitoring at Canaveral National Seashore: 2012 data summary. National Park Service, 2013. https://doi.org/10.36967/2195325.

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Abstract:
In 2007 the National Park Service (NPS) Southeast Coast Network (SECN) began collecting water-quality data in the estuarine waters of Canaveral National Seashore as part the NPS Vital Signs monitoring program. The scope of the monitoring program includes Mosquito Lagoon and is comprised of continuous water-quality monitoring conducted by the SECN at one site and is augmented with monthly data collected at five stations by St. Johns River Water Management District (SJRWMD). The continuous-monitoring data station is located at the Canaveral National Seashore visitor center dock and collects pH,
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