{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/anomaly-detection/papers/45","list_of":"/task/anomaly-detection","task":"Anomaly Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":45,"pages_in_order":49,"rows_per_page":100,"rows":[4401,4500],"of":4856,"counts":{"archive_papers_tagged":4856,"with_a_code_link":1727,"where_syntology_ran_a_sample":347,"not_listed_spam_title":0,"listed":4856,"listed_where_code_ran":347,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":294,"every_run_a_failure_of_syntologys_instrument":53,"listed_with_a_run_with_no_instrument_failure":294,"listed_every_run_a_failure_of_syntologys_instrument":53,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/anomaly-detection","prev":"/task/anomaly-detection/papers/44","next":"/task/anomaly-detection/papers/46","papers":[{"url":null,"slug":"enhanced-cyber-physical-security-through-deep","title":"Enhanced Cyber-Physical Security through Deep Learning Techniques","date":"2019-08-23","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-recurrent-reconstructive","title":"Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection","date":"2019-08-22","arxiv_id":"1908.08204","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807654","title":"FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans","date":"2019-08-21","arxiv_id":"1908.07654","repositories_listed":0,"syntology":null},{"url":null,"slug":"cbowra-a-representation-learning-approach-for","title":"CBOWRA: A Representation Learning Approach for Medication Anomaly Detection","date":"2019-08-20","arxiv_id":"1908.07147","repositories_listed":0,"syntology":null},{"url":null,"slug":"wifi-motion-detection-a-study-into-efficacy","title":"WiFi Motion Detection: A Study into Efficacy and Classification","date":"2019-08-20","arxiv_id":"1908.08476","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-shilling-attack-based-on-t","title":"Detection of Shilling Attack Based on T-distribution on the Dynamic Time Intervals in Recommendation Systems","date":"2019-08-18","arxiv_id":"1908.06967","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-abnormalities-in-resting-state","title":"Detecting abnormalities in resting-state dynamics: An unsupervised learning approach","date":"2019-08-16","arxiv_id":"1908.06168","repositories_listed":0,"syntology":null},{"url":null,"slug":"gods-generalized-one-class-discriminative","title":"GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection","date":"2019-08-16","arxiv_id":"1908.05884","repositories_listed":0,"syntology":null},{"url":"/paper/multi-timescale-trajectory-prediction-for","slug":"multi-timescale-trajectory-prediction-for","title":"Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection","date":"2019-08-12","arxiv_id":"1908.04321","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-structured-cross-modal-anomaly-detection","title":"Deep Structured Cross-Modal Anomaly Detection","date":"2019-08-11","arxiv_id":"1908.03848","repositories_listed":0,"syntology":null},{"url":null,"slug":"specae-spectral-autoencoder-for-anomaly","title":"SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks","date":"2019-08-11","arxiv_id":"1908.03849","repositories_listed":0,"syntology":null},{"url":null,"slug":"transcriptional-response-of-sk-n-as-cells-to","title":"Transcriptional Response of SK-N-AS Cells to Methamidophos","date":"2019-08-11","arxiv_id":"1908.03841","repositories_listed":0,"syntology":null},{"url":null,"slug":"what-goes-around-comes-around-cycle","title":"What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks","date":"2019-08-08","arxiv_id":"1908.03055","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-optimal-sequencing-of","title":"Task-Oriented Optimal Sequencing of Visualization Charts","date":"2019-08-07","arxiv_id":"1908.02502","repositories_listed":0,"syntology":null},{"url":null,"slug":"abnormality-detection-in-musculoskeletal","title":"Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization","date":"2019-08-06","arxiv_id":"1908.02170","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-trend-inconsistency-a-prediction-driven","title":"Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality","date":"2019-08-03","arxiv_id":"1908.01146","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-representation-learning-and","title":"Unsupervised Representation Learning and Anomaly Detection in ECG Sequences","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"q-mind-defeating-stealthy-dos-attacks-in-sdn","title":"Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework","date":"2019-07-27","arxiv_id":"1907.11887","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-encoder-decoder-based-approach-for-anomaly","title":"An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing","date":"2019-07-26","arxiv_id":"1907.11778","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-aware-feature-for-improved-video","title":"Motion-Aware Feature for Improved Video Anomaly Detection","date":"2019-07-24","arxiv_id":"1907.10211","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-network-based-on-device-learning","title":"A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices","date":"2019-07-23","arxiv_id":"1907.10147","repositories_listed":0,"syntology":null},{"url":null,"slug":"camlpad-cybersecurity-autonomous-machine","title":"CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection","date":"2019-07-23","arxiv_id":"1907.10442","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-anomaly-detection-in-power-systems","title":"Early Anomaly Detection in Power Systems Based on Random Matrix Theory","date":"2019-07-21","arxiv_id":"1907.10485","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-correlation-analysis-of","title":"Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks","date":"2019-07-21","arxiv_id":"1810.08962","repositories_listed":0,"syntology":null},{"url":null,"slug":"batch-uniformization-for-minimizing-maximum","title":"Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds","date":"2019-07-19","arxiv_id":"1907.08338","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-adaptive-approach-for-anomaly-detector","title":"An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series","date":"2019-07-18","arxiv_id":"1907.07843","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-online-prediction-in-the-presence","title":"Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach","date":"2019-07-15","arxiv_id":"1907.06377","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-voice-pathology-detection","title":"Towards Robust Voice Pathology Detection","date":"2019-07-13","arxiv_id":"1907.06129","repositories_listed":0,"syntology":null},{"url":null,"slug":"amad-adversarial-multiscale-anomaly-detection","title":"AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data","date":"2019-07-12","arxiv_id":"1907.06582","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-real-time-anomaly-detection-in","title":"Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks","date":"2019-07-12","arxiv_id":"1907.05813","repositories_listed":0,"syntology":null},{"url":null,"slug":"fortuneteller-predicting-microarchitectural","title":"FortuneTeller: Predicting Microarchitectural Attacks via Unsupervised Deep Learning","date":"2019-07-08","arxiv_id":"1907.03651","repositories_listed":0,"syntology":null},{"url":null,"slug":"networkmetrics-unraveled-mbda-in-action","title":"Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring","date":"2019-07-05","arxiv_id":"1907.02677","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-anomalous-trajectory-detection","title":"Unsupervised Anomalous Trajectory Detection for Crowded Scenes","date":"2019-07-03","arxiv_id":"1907.01717","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improvement-of-paa-on-trend-based","title":"An Improvement of PAA on Trend-Based Approximation for Time Series","date":"2019-06-28","arxiv_id":"1907.00700","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-subsequence-detection-with-dynamic","title":"Anomaly Subsequence Detection with Dynamic Local Density for Time Series","date":"2019-06-28","arxiv_id":"1907.00701","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-task-learning-for-anomalous","title":"Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data","date":"2019-06-28","arxiv_id":"1907.00749","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-and-statistical-modeling-of-birth","title":"Detection and Statistical Modeling of Birth-Death Anomaly","date":"2019-06-27","arxiv_id":"1906.11788","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-big-data-analysis-for-intrusion","title":"Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle","date":"2019-06-27","arxiv_id":"1906.11976","repositories_listed":0,"syntology":null},{"url":null,"slug":"addgraph_-anomaly-detection-in-dynamic-graph","title":"AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN","date":"2019-06-24","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"in-vehicle-false-information-attack-detection","title":"Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network","date":"2019-06-24","arxiv_id":"1906.10203","repositories_listed":0,"syntology":null},{"url":null,"slug":"normalizing-flows-for-novelty-detection-in","title":"Normalizing flows for novelty detection in industrial time series data","date":"2019-06-17","arxiv_id":"1906.06904","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-with-joint-representation","title":"Anomaly Detection with Joint Representation Learning of Content and Connection","date":"2019-06-16","arxiv_id":"1906.12328","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-with-hmm-gauge-likelihood","title":"Anomaly Detection with HMM Gauge Likelihood Analysis","date":"2019-06-14","arxiv_id":"1906.06134","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-based-multiple-adjacent-brain-mri-slice","title":"GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis","date":"2019-06-14","arxiv_id":"1906.06114","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-proximal-auc-maximization","title":"Stochastic Proximal AUC Maximization","date":"2019-06-14","arxiv_id":"1906.06053","repositories_listed":0,"syntology":null},{"url":null,"slug":"warping-resilient-time-series-embeddings","title":"Warping Resilient Scalable Anomaly Detection in Time Series","date":"2019-06-12","arxiv_id":"1906.05205","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-high-performance","title":"Anomaly Detection in High Performance Computers: A Vicinity Perspective","date":"2019-06-11","arxiv_id":"1906.04550","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenges-in-time-stamp-aware-anomaly","title":"Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos","date":"2019-06-11","arxiv_id":"1906.04574","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-spatio-temporal-data-mining","title":"Deep Learning for Spatio-Temporal Data Mining: A Survey","date":"2019-06-11","arxiv_id":"1906.04928","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-combination-of-temporal-sequence-learning","title":"A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS","date":"2019-06-07","arxiv_id":"1906.05277","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-anomaly-detection-to-support","title":"Using anomaly detection to support classification of fast running (packaging) processes","date":"2019-06-06","arxiv_id":"1906.02473","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600705","title":"An Adaptive Training-less System for Anomaly Detection in Crowd Scenes","date":"2019-06-03","arxiv_id":"1906.00705","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-series-anomaly-detection-using","title":"Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning","date":"2019-05-31","arxiv_id":"1905.13628","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-anomaly-detection-using-extreme","title":"Bayesian Anomaly Detection Using Extreme Value Theory","date":"2019-05-29","arxiv_id":"1905.12150","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-epistemic-uncertainty-of-anatomy","title":"Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT","date":"2019-05-29","arxiv_id":"1905.12806","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexible-mining-of-prefix-sequences-from-time","title":"Learning Temporal Causal Sequence Relationships from Real-Time Time-Series","date":"2019-05-29","arxiv_id":"1905.12262","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-machine-learning-based-anomaly","title":"Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set","date":"2019-05-28","arxiv_id":"1905.11757","repositories_listed":0,"syntology":null},{"url":null,"slug":"pnunet-anomaly-detection-using-positive-and","title":"PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure","date":"2019-05-27","arxiv_id":"1905.10939","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lipschitz-constrained-anomaly-discriminator","title":"Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators","date":"2019-05-26","arxiv_id":"1905.10710","repositories_listed":0,"syntology":null},{"url":null,"slug":"devil-in-the-detail-attack-scenarios-in","title":"Devil in the Detail: Attack Scenarios in Industrial Applications","date":"2019-05-24","arxiv_id":"1905.10292","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-collection-and-forecasting-of-resource","title":"Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems","date":"2019-05-22","arxiv_id":"1905.09219","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-ensembles-of-anomaly-detectors-on","title":"Learning Ensembles of Anomaly Detectors on Synthetic Data","date":"2019-05-20","arxiv_id":"1905.07892","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-multivariate-anomaly-detection-and","title":"Online Multivariate Anomaly Detection and Localization for High-dimensional Settings","date":"2019-05-17","arxiv_id":"1905.07107","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-analysis-of-traffic-camera-data","title":"Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection","date":"2019-05-17","arxiv_id":"1905.07332","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-rats-in-cats-detecting-stealthy","title":"Finding Rats in Cats: Detecting Stealthy Attacks using Group Anomaly Detection","date":"2019-05-16","arxiv_id":"1905.07273","repositories_listed":0,"syntology":null},{"url":null,"slug":"which-principal-components-are-most-sensitive","title":"Which principal components are most sensitive to distributional changes?","date":"2019-05-15","arxiv_id":"1905.06318","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-anomaly-detection-with-sparse-gaussian","title":"Online Anomaly Detection with Sparse Gaussian Processes","date":"2019-05-14","arxiv_id":"1905.05761","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-bursty-announcements-for-early","title":"Using Bursty Announcements for Detecting BGP Routing Anomalies","date":"2019-05-14","arxiv_id":"1905.05835","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-analytics-of-anomalous-user-behaviors","title":"Visual Analytics of Anomalous User Behaviors: A Survey","date":"2019-05-14","arxiv_id":"1905.06720","repositories_listed":0,"syntology":null},{"url":null,"slug":"attack-and-anomaly-detection-in-iot-sensors","title":"Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches","date":"2019-05-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"190503554","title":"1D Convolutional Neural Networks and Applications: A Survey","date":"2019-05-09","arxiv_id":"1905.03554","repositories_listed":0,"syntology":null},{"url":null,"slug":"190513147","title":"Anomaly Detection in Images","date":"2019-05-09","arxiv_id":"1905.13147","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-modal-one-class-generative","title":"A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"engan-latent-space-mcmc-and-maximum-entropy","title":"EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uainets-from-unsupervised-to-active-deep","title":"UaiNets: From Unsupervised to Active Deep Anomaly Detection","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-in-traffic-scenes-via","title":"Anomaly Detection in Traffic Scenes via Spatial-aware Motion Reconstruction","date":"2019-04-30","arxiv_id":"1904.13079","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-information-centrality-for","title":"Exploring Information Centrality for Intrusion Detection in Large Networks","date":"2019-04-27","arxiv_id":"1904.12138","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-anomaly-detection-in-images-to","title":"Reducing Anomaly Detection in Images to Detection in Noise","date":"2019-04-25","arxiv_id":"1904.11276","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-study-of-credit-card-fraud","title":"A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised","date":"2019-04-24","arxiv_id":"1904.10604","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-augmented-text-anomaly-detection-with","title":"GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics","date":"2019-04-24","arxiv_id":"1904.11094","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-representation-learning-for-social","title":"Deep Representation Learning for Social Network Analysis","date":"2019-04-18","arxiv_id":"1904.08547","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-method-for-anomaly-detection-in","title":"Graph-Based Method for Anomaly Prediction in Brain Network","date":"2019-04-15","arxiv_id":"1904.07163","repositories_listed":0,"syntology":null},{"url":null,"slug":"should-i-raise-the-red-flag-a-comprehensive","title":"Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms","date":"2019-04-14","arxiv_id":"1904.06646","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-learning-in-statistical","title":"Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks","date":"2019-04-12","arxiv_id":"1904.06292","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-anomaly-detection-based-on-deep","title":"Supervised Anomaly Detection based on Deep Autoregressive Density Estimators","date":"2019-04-12","arxiv_id":"1904.06034","repositories_listed":0,"syntology":null},{"url":null,"slug":"software-based-higher-order-structural-foot","title":"Software Based Higher Order Structural Foot Abnormality Detection Using Image Processing","date":"2019-04-11","arxiv_id":"1904.05651","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-system-trace-restoration","title":"Deep Learning for System Trace Restoration","date":"2019-04-10","arxiv_id":"1904.05411","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-a-dual-convolutional-neural","title":"Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery","date":"2019-04-10","arxiv_id":"1904.05304","repositories_listed":0,"syntology":null},{"url":null,"slug":"place-specific-background-modeling-using","title":"Place-specific Background Modeling Using Recursive Autoencoders","date":"2019-04-07","arxiv_id":"1904.03555","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-based-method-for-cyber-intrusion","title":"Efficient GAN-based method for cyber-intrusion detection","date":"2019-04-04","arxiv_id":"1904.02426","repositories_listed":0,"syntology":null},{"url":"/paper/learning-representations-from-healthcare-time","slug":"learning-representations-from-healthcare-time","title":"Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection","date":"2019-04-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-google-analytics-to-support","title":"Using Google Analytics to Support Cybersecurity Forensics","date":"2019-04-03","arxiv_id":"1904.01725","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-network-intrusion","title":"Active Learning for Network Intrusion Detection","date":"2019-04-02","arxiv_id":"1904.01555","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-network-intrusion-detection","title":"Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier","date":"2019-04-02","arxiv_id":"1904.01352","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-contextual-anomaly-detection","title":"Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models","date":"2019-04-01","arxiv_id":"1904.00548","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoding-binary-classifiers-for","title":"Autoencoding Binary Classifiers for Supervised Anomaly Detection","date":"2019-03-26","arxiv_id":"1903.10709","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatially-weighted-anomaly-detection-with","title":"Spatially-weighted Anomaly Detection with Regression Model","date":"2019-03-23","arxiv_id":"1903.09798","repositories_listed":0,"syntology":null},{"url":"/paper/ocgan-one-class-novelty-detection-using-gans","slug":"ocgan-one-class-novelty-detection-using-gans","title":"OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations","date":"2019-03-20","arxiv_id":"1903.08550","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-competitive-and-discriminative","title":"Learning Competitive and Discriminative Reconstructions for Anomaly Detection","date":"2019-03-17","arxiv_id":"1903.07058","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-stage-fault-warning-for-large-electric","title":"Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning","date":"2019-03-15","arxiv_id":"1903.06700","repositories_listed":0,"syntology":null}],"record_sha256":"5767a188aa4b418742bc5c5ba07cde61f01fee9af1380c491455eac29b6c6b3d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}