![Bloom filter [Bloom 1970] and variants (Neural Bloom Filter)
Count-Min [Cormode and Muthukrishnan 2005] and variants
Filter & COUNT Sketches
Dolha [Zhang et al. 2019], Spotlight [Eswaran et al. 2018]
TCM [Tang et al. 2016], gMatrix [Khan and Aggarwal 2016]
Graph Sketching
Random Sampling/Projections [Mahoney 2011]
Frequent Directions [Ghashami et al. 2016]Matrix Sketching
SKETCHING](https://image.slidesharecdn.com/adattheedge-200625054421/85/Anomaly-Detection-At-The-Edge-29-320.jpg)
![[Kim et al. 2017]
CNN-Variational Autoencoder
[Kim et al. 2017]
Squeezed Convolutional Variational
Autoencoder
[Lu and Lysecky, 2019]
One Class SVM
[Lin et al. 2019]
Edge-Based RNN
[Nguyen et al. 2019]
GRU
[Yang et al. 2019]
Federated XGBoost
MODELS for Anomaly detection at the Edge
Subcomponent timing
FEATURES](https://image.slidesharecdn.com/adattheedge-200625054421/85/Anomaly-Detection-At-The-Edge-31-320.jpg)
![[Mattia et al. 2019]
A Survey on GANs for Anomaly Detection
[Fadhel and Nyarko, 2019]
GAN Augmented Text Anomaly Detection with Sequences
of Deep Statistics
[Wen and Keyes 2019]
Time Series Anomaly Detection Using Convolutional
Neural Networks and Transfer Learning
[Pol et al. 2019]
Anomaly Detection with Conditional
Variational Autoencoders
[Wang et al. 2019]
adVAE: A self-adversarial variational autoencoder
with Gaussian anomaly prior knowledge for
anomaly detection
[Akçay et al. 2018]
GANomaly: Semi-Supervised Anomaly
Detection via Adversarial Training
[Tsukada et al. 2019]
A Neural Network-Based On-device Learning
Anomaly Detector for Edge Devices
[Bhatia et al. 2019]
MIDAS: Microcluster-Based Detector of
Anomalies in Edge Streams
READINGS
[Choudhary et al. 2017]
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data](https://image.slidesharecdn.com/adattheedge-200625054421/85/Anomaly-Detection-At-The-Edge-50-320.jpg)
![[Huan et al. 2019]
[Puzanov and Cohen 2019]
[Hannon et al. 2019]
[Maciąg et al. 2019]
[Calikus et al. 2019]
[Zhong et al. 2019]
Active anomaly detection in heterogeneous processes
Deep reinforcement one-shot learning for change point detection
Real-time Anomaly Detection and Classification in Streaming PMU Data
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks
No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection
Deep Actor-Critic Reinforcement Learning for Anomaly Detection
READINGS](https://image.slidesharecdn.com/adattheedge-200625054421/85/Anomaly-Detection-At-The-Edge-51-320.jpg)
![READINGS
[Nolle et al. 2020[
DeepAlign: Alignment-based Process
Anomaly Correction Using Recurrent
Neural Networks
[Li et al. 2020]
RCC-Dual-GAN: An Efficient Approach for
Outlier Detection with Few Identified
Anomalies
[Ngo et al. 2019]
Fence GAN: Towards Better
Anomaly Detection
[Ngo et al. 2020]
Adaptive Anomaly Detection for IoT Data
in Hierarchical Edge Computing
[Gao et al. 2020]
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks](https://image.slidesharecdn.com/adattheedge-200625054421/85/Anomaly-Detection-At-The-Edge-52-320.jpg)
Uploaded byArun Kejariwal
Anomaly Detection At The Edge
The document discusses edge computing, highlighting its role in anomaly detection and its potential to reduce network traffic by processing data closer to the source. It outlines various use cases across industries such as healthcare, agriculture, and finance, emphasizing the importance of low-latency processing and decentralized systems. Additionally, the document addresses the challenges of implementing edge computing, including data security, network reliability, and the need for efficient algorithms.
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Anomaly Detection At The Edge
- 1.
- 2. Computing closer to the data source, reduce network traffic SUPPLEMENT CLOUD COMPUTING 5G - 1.1B connections by 2023, 8.9% of all mobile device connections*^ NEWTECHNOLOGIES Sensors, actuators - distributed computing topology Internet of Things Network outages, machine downtime, and weather change Improve Accuracy of Events EDGE COMPUTING An Overview: What, Drivers, Example use cases * https://www.idc.com/getdoc.jsp?containerId=prUS45740019 (Dec 2019) ^ https://www.mckinsey.com/industries/advanced-electronics/our-insights/the-5g-era-new-horizons-for-advanced-electronics-and-industrial-companies (Feb 2020)
- 3. BUSINESS OPPORTUNITY CAGR OF 26.5% USD 9Bby 2024 * Low-latency processing Real-TIme Automated Decision making Low half-life or low value INCREASING DATA VOLUME Reduction in network traffic and compute needed EFFiciency * https://www.marketsandmarkets.com/Market-Reports/edge-computing-market-133384090.html (Aug 2019)
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- 7. Connected Cars - increase situational awareness Autonomous Vehicles Quality-of-Service (QoS) TELECOMMUNICATIONS Personalization - monitoring HEALTHCARE andLIFE SCIENCES Predictive Maintenance MANUFACTURING Hypertargeting RETAIL AND CONSUMER GOODS INDUSTRIES J
- 8. Remote health sensing to identify clusters of suspected illnesses Monitoring of early onset of the disease in mild patients (to quickly identify if hospitalization is needed) #COVID-19 @ @ @ Monitoring of lab testing devices and results (to check for false positives and false negatives)
- 9.
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- 12. Energy Efficiency, Smart Meters SMART HOMES Optimize route planning Stores and Restaurants Nearby SMART TRANSPORTATION Preprocessing - improve latency, reduce bandwidth requirement DATAREPORTING Air quality, water quality in lakes, rivers Environmental MonitorinG USE CASES
- 13. Voice control Conversational interfaces Robots, Drones AUTOMATION Real-time Monitoring HEALTH &SAFETY Data Filtering PRIVACY USE CASES
- 14.
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- 17. AI AT THEEDGE On Device Productivity LANGUAGE TRANSLATION Authentication Facial Recognition No checkout stores Vision Security Cameras Motion Detection Metaverses for the home, workplace, amusement park, school and social settings AR/VR Personalization Recommendations Enabling disconnected, local interactions
- 18. Pooling, Chunk-wise attention SpeeD-Accuracy trade-off Lightweight vocoders: SqueezeWave* Text→Acoustic features→Waveforms Tens of MB Small Model Size News (#COVID-19), Driving directions Text-to-Speech Hyper low-rank approximation^ Mixed Low precision Quantization# Optimizations Leverage hardware accelerators, e.g. DSPs Parallel algorithms On-DEVICE SPEECH RECOGNITION/Synthesis * “SqueezeWave: Extremely Lightweight Vocoders for On-device Speech Synthesis”, Zhai et al. 2020. ^ “Attention based on-device streaming speech recognition with large speech corpus”, Kim et al. 2020. # "Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers”, Rusci et al. 2019. Language recognition Speech reconstruction (Lip Reading) Visual Speech Recognition
- 19. COMPUTE CHALLENGES SPACE BATTERY LIFE intermittentPOWER Small form factor Withstand rugged environments (weather, vibration and connectivity) Sensor poor and noise Low power
- 20. Concept drift, Non-stationarity EnviRonmental changes MobileNets^, EdgeCNN# , ApproxNet, IONet, Fire SSD Inertial* - Accelerometer, Gyroscope, Magnetometer Temperature MODELS, FEATURES Fully Decentralized / Peer-to-Peer Distributed Learning Multi-agent optimization Federatedlearning INFERENCE, TRAINING * https://www.bosch-sensortec.com/products/motion-sensors/imus/ Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference, Chen et al. 2020 ^ https://arxiv.org/abs/1905.02244 # EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing, Yang et al. 2019 Compute and memory constrained Network unreliability, Low power Convergence rate Online (Re-) Training INFERENCE Velocity, Orientation, Trajectory, Activity Example: Pedestrian navigation
- 21. FEDERATED learning*^ Mobile keyboard, vocal classifiers, next word prediction Predicting future hospitalization, patient similarity learning Applications Share the weights, not the raw data Single hidden-layer FF networks, Autoencoders, Federated Momentum MODELS Handling unbalanced and non-IID data, Neural architecture search Multi-task learning, Domain adaption, Meta learning Improving Efficiencyand EffectiveNess# Convergence time, Communication between devices Bias in Training Data, Compliance (HIPAA, FERPA) Challenges * “Towards Federated Learning at Scale: System Design”, Bonawitz et al. 2019. ^ “A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection”, Li et al. 2019. # “Advances and Open Problems in Federated Learning“, Kairouz et al. 2019.
- 22. Emerging application requires <10 ms end-to-end latency, guarantee freshness of insights Low latency Increasingly support extraction of insights on high velocity data streams Data Velocity Moments of univariate/multivariate data, susceptibility to anomalies DescriptiveStatistics * Serverless architectures DISTRIBUTED ONE pASS ALGORITHMS * Numerically stable, scalable formulas for parallel and online computation of higher-order multivariate central moments with arbitrary weights, Pébay et al. 2016.
- 23.
- 24.
- 25.
- 26. LONG HISTory -Studied for over 125 yrs
- 27. Variational AE, Adversarial AE autoencodeR ARIMA and variants Kernel PCA, Robust PCA, Sparse PCA Time SERIES ANALYSIS,Pca Transformer-XL, Reformer Transformer BRNN, Stochastic RNN, Convolutional LSTM LSTM, GRU, GLU, GHN BiGAN, GANomaly GANs Sparse Attentive Backpropagation Attention STATISTICAl, DL/RL Techniques
- 28.
- 29. Bloom filter [Bloom1970] and variants (Neural Bloom Filter) Count-Min [Cormode and Muthukrishnan 2005] and variants Filter & COUNT Sketches Dolha [Zhang et al. 2019], Spotlight [Eswaran et al. 2018] TCM [Tang et al. 2016], gMatrix [Khan and Aggarwal 2016] Graph Sketching Random Sampling/Projections [Mahoney 2011] Frequent Directions [Ghashami et al. 2016]Matrix Sketching SKETCHING
- 30.
- 31. [Kim et al. 2017] CNN-Variational Autoencoder [Kim et al. 2017] Squeezed ConvolutionalVariational Autoencoder [Lu and Lysecky, 2019] One Class SVM [Lin et al. 2019] Edge-Based RNN [Nguyen et al. 2019] GRU [Yang et al. 2019] Federated XGBoost MODELS for Anomaly detection at the Edge Subcomponent timing FEATURES
- 32. The makeup ofan anomaly detection product and edge use cases 2
- 33.
- 34.
- 35. 5 PATENT US10061632B2 PATENTUS10061677B2 PATENT US20160210556A1 PATENT PENDING ANOMALY SCORE SEASONALITY LEADING DIMENSIONS HD BASELINE AT SCALE API requests for service 123 Drop in Play for app123 , source-promo Traffic for partnerAccount, partnerName Login Errors
- 36. Correlate What Drop in payments successrate Where Card: Visa Type: Online Why + Spike in API errors + New version release Payment success rate Payment API Errors
- 37. False Positive Reduction Mechanisms Problem: How doI reduce the alerts I receive without decreasing the quality of detection? Alert Simulation Influencing Factors Context and Correlation (Patented) Spot on alerts Anomaly Attributes Duration | Delta | Score (Patented) Alert Feedback Loop
- 38. Who is usingit? Enterprise Telecom Gaming Internet Fintech eCommerce Adtech
- 39. Why are theyusing it? Partner Monitoring Revenue and Cost Monitoring Customer Experience Monitoring DAU MAU Retention Usage Flows Funnels APIs Partners Affiliates Customers (b2b) 3rd party services Purchase/sales funnels Price & promo glitches Payment gateways Cloud costs Ad costs
- 40. But what aboutthe edge? COVID-19 is pushing this along...
- 41. ● Monitoring confirmed cases continuously even if they are at home. ● Monitoring hospitalized and ventilated patients at scale ● Alerts using static thresholds on health indicators (e.g., SPO2 < 90%) is too noisy and creates alert fatigue, and often late. ● Requirements: ○ Remote monitoring ○ Early warning score to identify deteriorating conditions without need for physical check The Problem - Volume of Monitored Patients
- 42. Real life examples:Detecting respiratory degradation from health watches Respiratory Rate for patient xxxx
- 43. Real life examples:ICU patient monitoring
- 44.
- 45. Benefits Scale Reduce load onmedical staff. Using an autonomous monitoring approach. Early Detection Improved outcomes to patients: the system is constantly monitoring them and alerts early on deterioration of condition Reduce risk of exposure Staff protection
- 46.
- 47. A LONG ROADAHEAD!
- 48.
- 49.
- 50.
- 51. [Huan et al. 2019] [Puzanov and Cohen 2019] [Hannon et al. 2019] [Maciąg et al. 2019] [Calikus et al. 2019] [Zhong et al. 2019] Active anomaly detectionin heterogeneous processes Deep reinforcement one-shot learning for change point detection Real-time Anomaly Detection and Classification in Streaming PMU Data Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection Deep Actor-Critic Reinforcement Learning for Anomaly Detection READINGS
- 52. READINGS [Nolle et al. 2020[ DeepAlign: Alignment-based Process AnomalyCorrection Using Recurrent Neural Networks [Li et al. 2020] RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies [Ngo et al. 2019] Fence GAN: Towards Better Anomaly Detection [Ngo et al. 2020] Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [Gao et al. 2020] RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
- 53. Understanding Anomaly Detection:An Exploration of Anomaly Detection's History, Applications, and State-of-the-Art Techniques https://www.marketsandmarkets.com/Market-Reports/edge-computing-market-133384090.html Edge Computing Market by by Component , Application, Organization Size https://www.globenewswire.com/news-release/2019/12/10/1958380/0/en/Edge-Computing-Market- worth-28-07-billion-by-2027-Exclusive-Report-by-Meticulous-Research.html Edge Computing Market worth $28.07 billion by 2027 https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/new-demand- new-markets-what-edge-computing-means-for-hardware-companies New demand, new markets: What edge computing means for hardware companies https://www.grandviewresearch.com/industry-analysis/edge-computing-market Edge Computing Market Size, Share & Trends Analysis Report https://www.gartner.com/doc/reprints?id=1-1OIB65YZ&ct=190917&st=sb Exploring the Edge: 12 Frontiers of Edge Computing http://shop.oreilly.com/product/0636920072904.do RESOURCES
- 54. RESOURCES https://blog.bosch-si.com/bosch-iot-suite/technical-capabilities-of-edge-computing-solution/ Technical capabilities ofan edge computing solution https://blog.bosch-si.com/internetofthings/why-edge-computing-for-iot/ Why edge computing for IoT? https://www.mckinsey.com/industries/advanced-electronics/our-insights/the-5g-era-new-horizons-for-advanced-electronics-and-industrial-companies The 5G era: New horizons for advanced-electronics and industrial companies https://www.idc.com/research/viewtoc.jsp?containerId=US46054020 IDC's Worldwide Core and Edge Computing Platforms Taxonomy, 2020 https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/connected-world-an-evolution-in-connectivity-beyond- the-5g-revolution Connected world: An evolution in connectivity beyond the 5G revolution