Chawla et al., 2020 - Google Patents
Interpretable unsupervised anomaly detection for RAN cell trace analysisChawla et al., 2020
View PDF- Document ID
- 6223543681414604817
- Author
- Chawla A
- Jacob P
- Feghhi S
- Rughwani D
- van der Meer S
- Fallon S
- Publication year
- Publication venue
- 2020 16th International Conference on Network and Service Management (CNSM)
External Links
Snippet
The high complexity of modern communication networks requires an increasing degree of automation for performance and fault management tasks. A key task is the classification and identification of anomalous operation modes (and faults). This is important to separate them …
- 238000001514 detection method 0 title abstract description 20
Classifications
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/14—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
- H04L41/147—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning for prediction of network behaviour
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/16—Network management using artificial intelligence
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Abbasi et al. | Deep learning for network traffic monitoring and analysis (NTMA): A survey | |
US11620528B2 (en) | Pattern detection in time-series data | |
Shahraki et al. | A comparative study on online machine learning techniques for network traffic streams analysis | |
US11356320B2 (en) | Identifying and locating a root cause of issues in a network having a known topology | |
US20200387797A1 (en) | Unsupervised outlier detection in time-series data | |
Pacheco et al. | Towards the deployment of machine learning solutions in network traffic classification: A systematic survey | |
Nedelkoski et al. | Anomaly detection and classification using distributed tracing and deep learning | |
US11522888B2 (en) | Anomaly detection and troubleshooting system for a network using machine learning and/or artificial intelligence | |
Chawla et al. | Interpretable unsupervised anomaly detection for RAN cell trace analysis | |
US20210390423A1 (en) | Deep fusion reasoning engine for time series analysis | |
Pektaş et al. | A deep learning method to detect network intrusion through flow‐based features | |
JP2023551029A (en) | Proactive anomaly detection | |
Salahuddin et al. | Chronos: Ddos attack detection using time-based autoencoder | |
Dou et al. | Pc 2 a: predicting collective contextual anomalies via lstm with deep generative model | |
Sundqvist et al. | Boosted ensemble learning for anomaly detection in 5G RAN | |
CN117763618A (en) | Visual-based secure database management system | |
Zhang et al. | Putracead: Trace anomaly detection with partial labels based on GNN and Pu Learning | |
Chawla et al. | Towards interpretable anomaly detection: Unsupervised deep neural network approach using feedback loop | |
Soukup et al. | Towards evaluating quality of datasets for network traffic domain | |
Pal et al. | DLME: distributed log mining using ensemble learning for fault prediction | |
Johari et al. | Anomaly detection and localization in nfv systems: an unsupervised learning approach | |
Fiandrino et al. | AIChronoLens: advancing explainability for time series AI forecasting in mobile networks | |
Lobo et al. | Drift detection over non-stationary data streams using evolving spiking neural networks | |
Feng et al. | Network anomaly early warning through generalized network temperature and deep learning | |
Monni et al. | An RBM anomaly detector for the cloud |