Nothing Special   »   [go: up one dir, main page]

Hadj-Kacem et al., 2020 - Google Patents

Anomaly prediction in mobile networks: A data driven approach for machine learning algorithm selection

Hadj-Kacem et al., 2020

Document ID
6071363712691176654
Author
Hadj-Kacem I
Jemaa S
Allio S
Slimen Y
Publication year
Publication venue
NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium

External Links

Snippet

In this paper, we propose a model for proactive anomaly detection in mobile networks. We show that when Key Performance Indicators (KPIs) are highly correlated, the linear regression gives a good accuracy in anomaly detection for a short prediction horizon. When …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity

Similar Documents

Publication Publication Date Title
O'Reilly et al. Anomaly detection in wireless sensor networks in a non-stationary environment
Ciocarlie et al. Detecting anomalies in cellular networks using an ensemble method
Hadj-Kacem et al. Anomaly prediction in mobile networks: A data driven approach for machine learning algorithm selection
US20160381580A1 (en) Association rule analysis and data visualization for mobile networks
CN105325023B (en) Method and the network equipment for cell abnormality detection
US11966319B2 (en) Identifying anomalies in a data center using composite metrics and/or machine learning
WO2015172657A1 (en) System and method for anomaly detection
CN104301895A (en) Double-layer trigger intrusion detection method based on flow prediction
Ciocarlie et al. On the feasibility of deploying cell anomaly detection in operational cellular networks
US20220334904A1 (en) Automated Incident Detection and Root Cause Analysis
CN116683588B (en) Lithium ion battery charge and discharge control method and system
Ali-Tolppa et al. Self-healing and resilience in future 5G cognitive autonomous networks
Mukherjee A novel strategy for locational detection of false data injection attack
EP3549366B1 (en) Forcasting time series data
Bodrog et al. A robust algorithm for anomaly detection in mobile networks
Zhang et al. Faulty sensor data detection in wireless sensor networks using logistical regression
CN114338351B (en) Network anomaly root cause determination method and device, computer equipment and storage medium
Bertalanic et al. A deep learning model for anomalous wireless link detection
CN103957547A (en) Node reputation evaluation method and system for wireless sensor network
Moshtaghi et al. Exponentially weighted ellipsoidal model for anomaly detection
Giampieri et al. A supervised classification system based on evolutive multi-agent clustering for smart grids faults prediction
Miao et al. MSFS: multiple spatio-temporal scales traffic forecasting in mobile cellular network
Horsmanheimo et al. NES—Network Expert System for heterogeneous networks
EP4075277A1 (en) Automated incident detection and root cause analysis
Kassan et al. Robustness analysis of hybrid machine learning model for anomaly forecasting in radio access networks