Suchergebnisse - "real-time anomaly detection"

Proceedings of 1st GENZERO Workshop

Weitere Verfasser: Andreoni, Martin Thakkar, Shreekant

Schlagwörter: Open Access, Autonomous Systems, Generative AI, Large Language Models, Zero Trust Architecture, CyberPhysical Systems, UAV Swarms, Secure Communications, Distributed Intelligence, Real-Time Anomaly Detection, Machine Learning, Robotics, Security and Resilience, AI-Driven Approaches, Secure Autonomy, Safety-Critical Systems, Fault Mitigation, Swarm Intelligence, Secure Computing, Autonomous Robotics, Artificial intelligence, Machine learning, Security and fire alarm systems, Computer security, Transport technology and trades

Dateibeschreibung: application/pdf

Relation: Artificial Intelligence (R0)

Zugangs-URL: https://library.oapen.org/handle/20.500.12657/108702

A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests

Autoren: Subhadip Bandyopadhyay Joy Bose Sujoy Roy Chowdhury

Quelle: International Journal of Mathematical, Engineering and Management Sciences, Vol 10, Iss 3, Pp 777-796 (2025)

Schlagwörter: telecom network monitoring, FOS: Computer and information sciences, Technology, Computer Science - Machine Learning, real-time anomaly detection, I.2.6, I.2.7, G.3, 62M10, 62P30, 68T07, data drift detection, H.2.8, H.3.3, Machine Learning (cs.LG), hybrid machine learning models, ai powered data drift detection, QA1-939, hierarchical temporal memory (htm), time series, Mathematics, sequential probability ratio test (sprt), streaming data analysis

Zugangs-URL: http://arxiv.org/abs/2504.18599
https://doaj.org/article/64d306aa2fe347eb8fa0d5c9f0c2af63
https://doi.org/10.33889/IJMEMS.2025.10.3.039

Integrating edge computing, data science and advanced cyber defense for autonomous threat mitigation

Autoren: Ajayi, Rhoda Masunda, Martha

Quelle: International Journal of Science and Research Archive. 15:063-080

Schlagwörter: Machine Learning for Cybersecurity, Edge Computing Security, Autonomous Threat Mitigation, Cyber Defense Architecture, Federated Learning in Security, Real-Time Anomaly Detection

Real-time Early Warning of Trading Behavior Anomalies in Financial Markets: An AI-driven Approach

Autoren: Dong, Boyang Trinh, Toan Khang

Quelle: Journal of Economic Theory and Business Management; Vol. 2 No. 2 (2025); 14-23

Schlagwörter: Machine Learning, Market Surveillance, Real-time Anomaly Detection, Investment Banking, Trading Behavior Analytics

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.suaspress.org/ojs/index.php/JETBM/article/view/v2n2a03

Next generation AI powered framework for autonomous energy optimization and real time anomaly detection in IoT driven wireless sensor networks

Autoren: M. Parameswari Nancy P R. Jeya Malar

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-26 (2025)

Schlagwörter: Next-generation AI, Autonomous energy optimization, Real-time anomaly detection, IoT-driven wireless sensor networks, Artificial intelligence, Energy efficiency, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/904c5a792f574475bbfc04cddd499965

Cybersecurity in Digital India: Enhancing UPI Security with AI-Driven Behavioral Biometrics

Autoren: Krisha Patel

Schlagwörter: Swipe gesture analysis, Continuous authentication, Unified Payments Interface (UPI), Keystroke dynamics, Transaction fraud prevention, Real-time anomaly detection, Unsupervised learning, AI-driven authentication, Fraud detection, Machine learning, User behavior analysis, Digital payment security, Behavioral biometrics, Supervised learning

Novel metrics and LSH algorithms for unsupervised, real-time anomaly detection in multi-aspect data streams

Autoren: Samira Khodabandehlou Alireza Hashemi Golpayegani

Quelle: Engineering Science and Technology, an International Journal, Vol 69, Iss, Pp 102119-(2025)

Schlagwörter: Locality-sensitive hashing, Multi-aspect data, Market manipulation detection, TA1-2040, Real-time anomaly detection, Engineering (General). Civil engineering (General), Unsupervised learning, Stream mining

Zugangs-URL: https://doaj.org/article/c35e955e25204e20b6764afa3f384b2c

AI-Based Predictive Maintenance in Computer Engineering: Enhancing System Reliability Under the Moderating Role of Edge Computing Integration

Autoren: Hoang Minh Quan Tran Minh Khang

Quelle: Journal of Economics, Finance And Management Studies.

Schlagwörter: AI-based predictive maintenance, Edge computing, Failure pattern detection, System reliability, Real-time anomaly detection

Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing

Autoren: My Driss Laanaoui Mohamed Lachgar Hanine Mohamed et al.

Quelle: IEEE Access, Vol 12, Pp 63683-63700 (2024)

Schlagwörter: real-time anomaly detection, Urban traffic management, intelligent transportation systems, 0502 economics and business, 05 social sciences, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, traffic density prediction, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, TK1-9971

Zugangs-URL: https://doaj.org/article/1ecfa19595934b11926b08e13e0cdc06

Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms

Autoren: Dániel László Vajda Tien Van Do Tamás Bérczes et al.

Quelle: Scientific Reports, Vol 14, Iss 1, Pp 1-22 (2024)

Schlagwörter: Server farm telemetry, Real-time anomaly detection, Periodic data, Pre-processing, AnDePeD, AnDePeD Pro, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/54ac89a622284a20a8f93eca60c0c847

SMART DShot: Secure Machine-Learning-Based Adaptive Real-Time Timing Correction

Autoren: Hyunmin Kim Zahid Basha Shaik Kadu Kyusuk Han

Quelle: Applied Sciences, Vol 15, Iss 15, p 8619 (2025)

Schlagwörter: artificial intelligence security, embedded machine learning, cyber–physical systems, motor control security, real-time anomaly detection, DShot protocol security, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/15/8619; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/3f6fc1b819494b1bbdf5f94f618d32e5

Enhancing Security and Privacy in IoT Data Streams: Real-Time Anomaly Detection for Threat Mitigation in Traffic Management

Autoren: Oumayma Berraadi Hicham Gibet Tani Mohamed Ben Ahmed

Quelle: Computer Sciences & Mathematics Forum, Vol 10, Iss 1, Pp 8-0 (2025)

Schlagwörter: IoT data streams, threat mitigation, real-time anomaly detection, IoT security, machine learning, traffic management, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2813-0324/10/1/8; https://doaj.org/toc/2813-0324

Zugangs-URL: https://doaj.org/article/e7e91e4696514c24b1f7deeb9b3f9114

Advanced Generative AI Models for Fraud Detection and Prevention in FinTech: Leveraging Deep Learning and Adversarial Networks for Real-Time Anomaly Detection in Financial Transactions

Autoren: Dixit, Sachin

Quelle: Journal of Artificial Intelligence Research; Vol. 4 No. 2 (2024): Journal of Artificial Intelligence Research; 51-77 ; 2583-7435

Schlagwörter: generative adversarial networks, fraud detection, FinTech, real-time anomaly detection, deep learning, financial transactions, adversarial networks, regulatory compliance

Dateibeschreibung: application/pdf

Relation: https://thesciencebrigade.com/JAIR/article/view/402/376; https://thesciencebrigade.com/JAIR/article/view/402

Verfügbarkeit: https://thesciencebrigade.com/JAIR/article/view/402

OQIMLI MA‘LUMOTLARNI QAYTA ISHLASHDA ANOMALIYALARNI ANIQLASH UCHUN ADAPTIVE THRESHOLD MEXANIZMINI ISHLATISH

Autoren: Hotamov , F.N.

Quelle: Science and Innovation; Vol. 3 No. 40 (2025): Science and Innovation; 26-28 ; Наука и инновация; Том 3 № 40 (2025): Наука и инновация; 26-28

Schlagwörter: adaptive threshold, sliding window, real-time anomaly detection

Dateibeschreibung: application/pdf

Relation: https://in-academy.uz/index.php/si/article/view/62945/39602

Verfügbarkeit: https://in-academy.uz/index.php/si/article/view/62945

Real-Time Anomaly Detection in Manufacturing: A Data-Driven Approach to Enhancing Production Efficiency through Online KPI Monitoring

Autoren: Gram, Jonas Drees, Jörg Stambera, Steffi et al.

Schlagwörter: Industry 4.0, Machine Learning, Real-Time Anomaly Detection, Smart Manufacturing

Relation: Conference on Manufacturing Systems 2025; Procedia CIRP; #PLACEHOLDER_PARENT_METADATA_VALUE#; https://publica.fraunhofer.de/handle/publica/489921

Verfügbarkeit: https://publica.fraunhofer.de/handle/publica/489921
https://doi.org/10.1016/j.procir.2025.03.047

Hybrid Traditional and Deep Learning Approaches for Network Traffic Forecasting with Real-Time Anomaly Detection : A Compartive Study of ARIMA, LSTM, ARIM-LSTM Hybrid Model

Autoren: Mandaleeka, AVSS Vighnesh Bonthu, Sumanth

Schlagwörter: ARIMA, LSTM, TIMESERIES DATA, HYBRID MODEL ARIMA LSTM, NETWORK TRAFFIC PREDICTION, real time anomaly detection, Telecommunications, Telekommunikation, Communication Systems, Kommunikationssystem, Computer Systems, Datorsystem

Dateibeschreibung: application/pdf

Verfügbarkeit: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-28338

Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data

Autoren: Stahmann, Philip Rieger, Bodo

Schlagwörter: Data Analytics, Control Systems, Business Strategies, benchmark, industrie 4.0, real-time anomaly detection algorithms, streaming data

Dateibeschreibung: 7 pages; application/pdf

Relation: Proceedings of the 55th Hawaii International Conference on System Sciences; http://hdl.handle.net/10125/80106

Verfügbarkeit: http://hdl.handle.net/10125/80106
https://doi.org/10.24251/HICSS.2022.766

Real-time anomaly detection for very short-term load forecasting

Autoren: Jian LUO Tao HONG Meng YUE

Quelle: Journal of Modern Power Systems and Clean Energy, Vol 6, Iss 2, Pp 235-243 (2018)

Schlagwörter: Data cleansing, Very short-term load forecasting, TK1001-1841, Production of electric energy or power. Powerplants. Central stations, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, TJ807-830, 02 engineering and technology, Real-time anomaly detection, 7. Clean energy, Multiple linear regression, Renewable energy sources

Zugangs-URL: https://link.springer.com/content/pdf/10.1007%2Fs40565-017-0351-7.pdf
https://doaj.org/article/ee72f89010d04c75b2cb9c10070c788d
https://paperity.org/p/85629270/real-time-anomaly-detection-for-very-short-term-load-forecasting
https://www.osti.gov/pages/biblio/1416032-real-time-anomaly-detection-very-short-term-load-forecasting
http://www.mpce.info/ch/reader/view_abstract.aspx?file_no=201802006&flag=1
https://link.springer.com/article/10.1007/s40565-017-0351-7
https://link.springer.com/content/pdf/10.1007%2Fs40565-017-0351-7.pdf
https://ieeexplore.ieee.org/abstract/document/9024711/

A Distributed Anomaly Detection System for In-Vehicle Network Using HTM

Autoren: Chundong Wang Zhentang Zhao Liangyi Gong et al.

Quelle: IEEE Access, Vol 6, Pp 9091-9098 (2018)

Schlagwörter: real-time anomaly detection, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, In-vehicle network security, HTM algorithm, TK1-9971

Dateibeschreibung: application/pdf

Zugangs-URL: https://eprints.mdx.ac.uk/24573/1/08274979.pdf
https://doaj.org/article/b929a37865624fddbe1e1deca0b7da5d
https://ieeexplore.ieee.org/abstract/document/8274979/
https://dblp.uni-trier.de/db/journals/access/access6.html#WangZGZLC18
https://doi.org/10.1109/ACCESS.2018.2799210
https://ieeexplore.ieee.org/document/8274979/

Real Time Data Anomaly Detection in OTT Streaming Data Services

Autoren: Arjun Mantri

Schlagwörter: Real-time anomaly detection, OTT streaming services, Machine learning, Spark Streaming, User activity monitoring

Relation: https://zenodo.org/records/13325634; oai:zenodo.org:13325634; https://doi.org/10.5281/zenodo.13325634

Verfügbarkeit: https://doi.org/10.5281/zenodo.13325634
https://zenodo.org/records/13325634