NSIBF detects anomalies in CPS using neural system identification and Bayesian filtering.
problem Detecting anomalies in CPS with complex dynamics and sensor noise.
method Neural System Identification and Bayesian Filtering (NSIBF).
result NSIBF outperforms state-of-the-art methods in anomaly detection for CPS.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
Adversarial attacks can fool copyright detection systems.
problem Vulnerability of copyright detection systems to adversarial attacks.
method Used gradient methods to create adversarial music that fooled detection systems.
result Adversarial attacks can successfully deceive industrial copyright detection tools.
Deep Autoencoder using GANs detects faults in closed loop systems without labeled data.
problem Fault detection in closed loop uncertain dynamical systems.
method Generative Adversarial Network (GAN) based Autoencoder.
result The proposed method significantly outperforms traditional classifier-based methods.
Paper presents attacks on real-time object detection systems.
problem Adversarial attacks on real-time object detection systems.
method Three targeted adversarial Objectness Gradient attacks (TOG).
result Adversarial attacks can cause object-vanishing, object-fabrication, and object-mislabeling.
EMODM detects abnormal patterns in complex systems.
problem Detecting abnormal patterns in complex systems.
method Probabilistic models and statistical algorithms.
result EMODM detects abnormal patterns in real-time raw data.
Survey on LSTM-based anomaly detection for technical systems.
problem Detect anomalies in technical systems due to complex dynamics.
method Use LSTM networks and other AI techniques to detect anomalies considering temporal and contextual characteristics.
result Demonstrates the potential of LSTM networks and graph-based approaches for anomaly detection.
A tree-based IDS detects cyber-attacks in AV networks.
problem Cyber-attacks in AV and IoV networks.
method Tree-structure machine learning models, ensemble learning, feature selection.
result High detection rate and low computational cost achieved.
New online learning algorithms improve cyberattack detection in industrial control systems.
problem Detecting cyberattacks in industrial control systems with limited resources.
method Online learning algorithms to process continuous data streams and address class imbalance.
result Improved detection rate of cyberattacks in industrial control systems.
Paper improves detection accuracy using privileged training data.
problem Traditional detection systems rely on runtime features, limiting model accuracy.
method Adapting knowledge transfer, model influence, and distillation techniques to use training-time privileged information.
result Empirical evaluation shows up to 16.9% relative decrease in detection error.
Real-time detection of out-of-distribution data in CPS control systems.
problem Detecting out-of-distribution data in CPS control systems for safety.
method Inductive conformal prediction and anomaly detection using variational autoencoders and deep support vector data description.
result Efficient real-time detection with low false alarm rates and comparable execution time.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
Paper monitors system state sequences to detect and assess deviations.
problem Detecting and evaluating deviations in dynamic systems.
method Data reduction, symbolic representation, anomaly detection, Markov Chains, generalized Jensen-Shannon Divergence.
result The approach detects and assesses system deviations probabilistically.
TitAnt detects online transaction fraud in milliseconds.
problem Real-time detection of online transaction fraud in e-commerce.
method Feature extraction, real-time prediction, deployment in Ant Financial.
result Efficient real-time fraud detection in milliseconds.
RNN with attention detects system anomalies with high accuracy.
problem Lack of interpretability in deep learning models for system log anomaly detection.
method Recurrent Neural Network (RNN) with attention mechanisms.
result RNN with attention achieves high performance in anomaly detection.
Complete CAD system for breast cancer detection from mammograms.
problem Reducing human errors in breast cancer detection.
method Algorithm development for image enhancement, mass and microcalcifications detection, and system architecture design.
result Novel algorithms for mass and microcalcifications detection with superior accuracy.
Proposes combining SARIMA and STL for real-time anomaly detection.
problem Need for accurate and fast anomaly detection systems for massive data.
method Combines SARIMA and STL models for anomaly detection.
result Demonstrates high accuracy in detecting anomalies in noisy, non-periodic data.
Detects anomalies in noisy data from linear systems.
problem Identifying samples of noise in a linear dynamical system.
method Robust spectral filtering and anomaly detection method.
result Guaranteed statistical performance in identifying noise samples.
RAID algorithm detects anomalies in real-time IoT systems.
problem Anomaly detection limitations in multivariate dynamic processes.
method Adapts to non-stationary effects and handles data drift.
result Improved detection accuracy and root cause isolation.
Paper proposes an anomaly detection system for DBMS diagnosis.
problem Difficulty in detecting anomalies in DBMS due to increasing metrics.
method Uses deep autoencoder and statistical process control for anomaly detection, and time series similarity for event finding.
result Demonstrates effectiveness of the proposed model in detecting anomalies and finding related events.
Improved speaker verification system detects replay attacks.
problem Detecting replay attacks in speaker verification systems.
method Multi-task learning of noise classes (replay noise, recording environment, recording device noises) and spoofing detection.
result Performance improved by 30% on ASVspoof2017 datasets.
Real-time anomaly detection and prediction system using Bayesian Networks.
problem Detect and predict anomalies in real-time systems.
method Develops a supervised machine learning model using Bayesian Network framework.
result Simultaneous anomaly detection and prediction achieved.
New method detects anomalies in systems influenced by their environment.
problem Detecting anomalies in systems under environmental influence.
method Adversarial learning and time series representation learning.
result Successfully addresses label sparsity and subjectivity in anomaly detection.
New framework detects policy changes in black-box DMS over time.
problem Lack of transparency in black-box decision making systems.
method Proposes temporal transparency, maps to time series changepoint detection, develops framework.
result Reveals policy changes in real-world DMS, including announced and unannounced.
Walmart developed anomaly detection for online pricing systems.
problem Early detection of mis-priced items in online retailers.
method Unsupervised and supervised approaches for real-time and batch anomaly detection.
result The system detected the most important anomalies with high precision.
A fuzzy recommender system using PageRank for community detection.
problem Improving recommendation systems for large user communities.
method Fuzzy community detection using personalized PageRank.
result The method outperforms recent recommender systems on MovieLens and FilmTrust datasets.
New PCA method detects faults using occupation kernels.
problem Fault detection in dynamical systems.
method Occupation kernel PCA for irregularly sampled data.
result Validation of reconstruction error approach for fault detection.
Integrates outlier detection into neural networks for improved performance.
problem Lack of competency awareness in machine learning systems, especially in detecting outliers.
method Null Space Analysis (NuSA) of neural networks, computing and controlling null space projection.
result NuSA-trained networks maintain classification performance and detect outliers effectively.
Paper proposes anomaly detection using Eigentraces and one-class classification.
problem Detect anomalies in system call trace data for Linux OS.
method One-class classification with Eigentraces feature extraction, Radial Basis Function neural network, and Random Forest.
result High performance in detecting anomalies and normal activities.
CMDNet simplifies MAP detection for large systems with probabilistic relaxation.
problem High complexity of MAP detection in large systems.
method Probabilistic Continuous relaxation of discrete variables, iterative CMD algorithm, CMDNet with online optimization.
result CMDNet achieves a promising accuracy-complexity trade-off in MIMO systems.
A new system detects audio replay attacks with high accuracy.
problem Detecting and preventing audio replay attacks in speaker verification systems.
method Proposes Attentive Filtering Network combining attention-based filtering and ResNet classifier.
result Achieves EER of 8.99% on ASVspoof 2017 Version 2.0 dataset.
TODS automates time series outlier detection with customizable pipelines.
problem Automated detection of outliers in time series data.
method Modular system with 70 primitives for data processing, time series analysis, and detection algorithms. GUI and data-driven searcher for pipeline design.
result Automated discovery and construction of effective outlier detection pipelines.
This paper analyzes sound event detection in synthetic office audio, comparing different systems.
problem Comparing sound event detection systems in synthetic office audio.
method Analysis of systems submitted to DCASE 2016 task, using synthetic office sounds.
result Statistical analysis of results, highlighting system performance under controlled conditions.
Paper tackles anomaly detection and RCA in dynamical systems using ICODE Networks.
problem Anomalies in dynamical systems impact performance and reliability.
method Proposes ICODE Networks for anomaly detection, RCA, and type classification.
result Demonstrates the ability to accurately detect anomalies, classify types, and pinpoint origins.
Unified framework detects changes in complex system models.
problem Accurate identification of dynamic changes in simulation models.
method Combines machine learning and process-driven simulation modeling.
result Significantly improves change point detection accuracy.
Paper extends SI method for detecting CPs in complex systems' frequency domain.
problem Identifying change points in complex systems' frequency domain.
method Extends SI framework to frequency domain using DFT properties and develops valid p-values.
result Reliable detection of genuine CPs with strong statistical guarantees.
Proposes a novel Out-of-Bag anomaly detection method for ML systems.
problem Challenges of detecting data anomalies in real-world datasets.
method Model-based approach decomposing unsupervised problem into ensemble models using Out-of-Bag estimates.
result Demonstrates state-of-the-art performance and improved accuracy in ML systems.
PyODDS is a Python system for outlier detection in databases.
problem Outlier detection in databases for various users.
method End-to-end Python system with database support, including statistical and deep learning algorithms.
result Supports outlier detection algorithms for users with or without machine learning background.
SAAD enhances anomaly detection in automotive systems with high accuracy.
problem Anomaly detection in automotive systems.
method Integrates statistical techniques with machine learning.
result SAAD achieves 88.3% accuracy and 0.921 F1 score.
Paper tackles anomaly detection in driving data, improving AI system performance.
problem Detecting rare anomalous driving situations in safety-critical AI systems.
method Semi-supervised multi-task learning using maneuver labels for imbalanced data.
result Improved anomaly detection performance on real-world driving data.
Automated methods detect errors in XML electronic dictionaries via statistical anomalies.
problem Errors in XML electronic dictionaries, especially in fields like text.
method Statistical anomaly detection using various signals like uncommon characters, text length, and language models.
result Automated systems improve efficiency in detecting errors in XML electronic dictionaries.
Anomaly detection method using multimodal autoencoder and sparse optimization.
problem Interpreting anomalies detected by deep learning models in complex ICT systems.
method Proposes a sparse optimization algorithm to estimate contributing dimensions of anomalies detected by multimodal autoencoders.
result Superiority of the proposed estimation algorithm in specifying contributing dimensions of anomalous data and detecting anomalies in cross-domain data.
Proposes novel method for detecting novel scenarios in autonomous systems.
problem Detecting when a machine learning model makes a trustworthy prediction in dynamic, real-world situations.
method Leverages trained model's learned information and a new image similarity metric.
result Demonstrates the method's efficacy on real-world driving and indoor racing datasets.
Community detection improves stock market portfolio optimization.
problem Improving portfolio optimization in financial markets.
method Community detection in correlation-based networks of worldwide stock markets.
result Portfolios constructed using community detection outperform traditional methods.
Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.
problem Challenges in MIMO detection due to interference and noise in high-order high-dimensional systems.
method The approach uses a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and Expectation Propagation (EP) messages to improve detection accuracy.
result The proposed algorithm outperforms state-of-the-art detection algorithms while maintaining low computational complexity.
The paper proposes a fast anomaly detection method for high-dimensional systems.
problem Real-time detection of anomalies in high-dimensional systems.
method Sequential and multivariate anomaly detection method, data-driven, semi-supervised.
result The method can quickly and accurately detect anomalies, including changes in correlation structure and stealth attacks.
End-to-end lung nodule detection system improves sensitivity and performance.
problem Detecting subtle lung nodules in raw CT data.
method Deep reconstruction network followed by 3D-CNN for nodule detection.
result End-to-end detector achieves comparable sensitivity to fully-sampled data.
Real-time fall detection system using DVS-TN on low-computing hardware.
problem Real-time fall detection for elderly care.
method DVS-TN optimized deep learning network for DVS, DVS Falls Dataset.
result 95.5% F1-score, 31.25 FPS on NVIDIA Jetson TX1.