Survey of methods to reduce false alarms in IDSs and ADSs.
problem High false alarms in IDSs and ADSs overwhelm administrators.
method Customized anomaly scoring and filtering methods.
result Promising techniques for reducing false alarms exist.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
Trend · papers per month
48 results for “false alarm mitigation”
Optimizes latency and false alarm probability in change detection problems.
problem Balancing latency and false alarms in non-stationary environments.
method Develops order-optimal change detectors under specified latency and false alarm levels.
result Derives a universal lower bound on latency and develops order-optimal detectors.
New models extrapolate false alarms in ASV without new data.
problem Reliable extrapolation of false alarm rates in ASV without new speaker data.
method Generative models in ASV score space for arbitrary systems.
result Models accurately extrapolate false alarm rates for large speaker populations.
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representationcs.LG
Autoencoder detects subtle changes in time series data.
problem Detect abrupt changes in time series data with high accuracy.
method Autoencoder with time-invariant representation and postprocessing.
result Outperforms baseline methods on various data sets.
Paper tackles ICU false alarms by learning features from ECG signals.
problem High rate of false alarms in ICU due to patient movements and sensor detachment.
method Unsupervised feature learning to extract and cluster high-level features from ECG signals.
result The method reduces false arrhythmia alarms using a few high-level features from a single ECG lead.
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when few…
Improved outbreak detection using machine learning fusion of statistical algorithms.
problem Balancing detection of outbreaks with false alarms.
method Train a fusion classifier using p-values and additional features.
result Fusion classifier using p-values and additional features improves outbreak detection.
KQT-EWMA monitors multivariate data streams online with flexible and practical change detection.
problem Online monitoring of multivariate data streams for detecting changes.
method Combines Kernel-QuantTree histogram and EWMA statistic for non-parametric monitoring.
result Controls Average Run Length (ARL0) while achieving comparable detection delays.
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual alarms if their algorithms indicate that a patient's condition may be worsening. However, current monitoring systems are extremely sensitive …
The paper introduces a framework for prescriptive process monitoring that generates alarms to prevent or mitigate undesired outcomes.
problem Existing predictive process monitoring techniques do not prescribe when and how to intervene to decrease undesired outcomes.
method The paper proposes a framework that extends predictive monitoring with the ability to generate alarms, incorporating a cost model to assess the trade-off between generating alarms and the cost of undesired outcomes.
result The net cost of undesired outcomes can be minimized by optimizing the generation of alarms based on the progress of the process instance and introducing delays for triggering alarms.
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processesstat.ME
New method detects and locates changes in spatio-temporal point processes.
problem Detecting and localizing changes in spatio-temporal data.
method Score-based, likelihood-free approach estimating change time and region.
result The method provides theoretical guarantees on detection and localization accuracy.
Detects harmful shifts without labels for model performance.
problem Detecting distribution shifts without access to labels.
method Uses a proxy derived from predictions of an error estimator.
result High power and false alarm control under various shifts.
Adaptive monitoring for AI systems detects and diagnoses shifts in data distribution.
problem Continuous monitoring of AI systems to detect and address unsafe behavior.
method Weighted-conformal martingales (WCTMs) for online monitoring of AI systems.
result Improved performance over state-of-the-art baselines on real-world datasets.
Balancing graph summarization and change detection in streaming data.
problem Balancing compression rate in graph summarization and accuracy in change detection.
method Introducing a probabilistic hierarchical latent variable model and optimizing parameters based on the minimum description length principle to balance the trade-off.
result Guaranteed suppression of Type I error probability (false alarms) in change detection.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average -NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly…
DeXposure-Claw supervises decentralized finance risks by grounding LLM decisions in evidence.
problem Weak evidence leads to over-interventions by general-purpose LLM agents in decentralized finance.
method DeXposure-Claw uses a graph time-series foundation model to forecast exposure networks, turning forecasts into alerts and constraining escalation with data-health gates.
result DeXposure-Claw reduces false alarms and improves regulator alignment in decentralized finance risk supervision.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on -point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as…
No-knowledge alarms detect misaligned LLM judges without trusting them.
problem Monitoring LLM judges for consistency when ground truth is unknown.
method Formal logic and Linear Programming to detect disagreements.
result No false positives in detecting misaligned judges.
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
problem Detecting changes in data sequences over time.
method Constructing and comparing backward and forward confidence sequences.
result The method successfully detects changes with strong nonasymptotic guarantees.
E-valuator converts verifier scores into reliable decision rules.
problem Ensuring the correctness of agent trajectories based on heuristic scores.
method Sequential hypothesis testing framework for online monitoring of agent trajectories.
result E-valuator provides better false alarm rate control and statistical power than other strategies.
Study speaker verification security using hierarchical Bayesian modeling.
problem Estimating false alarm rate in ASV systems for large speaker databases.
method Hierarchical Bayesian modeling of ASV scores to assess security against closest impostors.
result Neither i-vector nor x-vector systems are secure against increased impostor database size.
Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to d…
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
problem Dynamic distribution shifts challenge real-world machine learning systems' risk assurances.
method Sequential hypothesis testing with 'testing by betting' to detect risk violations.
result Effective real-time risk monitoring under various unknown shifts.
Model change detection is studied, in which there are two sets of samples that are independently and identically distributed (i.i.d.) according to a pre-change probabilistic model with parameter , and a post-change model with parameter , respectively. The goal is to detect whether the change in the model is sign…
Novel online graph-based method detects changes in high-dimensional data.
problem Challenges in detecting changes in high-dimensional data.
method Graph-based similarity measure derived from graph-spanning ratio.
result High detection power and controlled false alarm rate for high-dimensional data.
Reduces change detection to estimation using confidence sequences.
problem Detecting changes in data streams with minimal delay and false alarms.
method Reduction from sequential change detection to sequential estimation using confidence sequences.
result Change detection scheme with minimal structural assumptions and strong guarantees.
Testing randomnessmath.PR
The paper reviews methods for testing randomness and exchangeability in sequential data.
problem Testing the randomness and exchangeability of sequential data.
method Conformal martingales for online testing of randomness and exchangeability.
result Valid and efficiency results for various conformal martingale-based procedures.
DeepAISE predicts sepsis onset with high accuracy and low false alarms.
problem Early prediction of sepsis in ICU patients to improve clinical situational awareness.
method Recurrent neural survival model that combines clinical criteria and treatment policies.
result DeepAISE produces the most accurate predictions (AUC=0.90 and 0.87) and lowest false alarm rates (FAR=0.20 and 0.26) compared to baseline models.
Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents. It is particularly challenging to maintain low false reject rate in real world conditions where there is (a) ambient noise from external sources s…
Geometric observables detect financial regime shifts with high accuracy.
problem Detecting regime shifts in financial markets.
method Extracted four geometric observables from equity-index returns and evaluated them against various baseline methods.
result The Berry Phase Rate achieves an unbiased out-of-sample median Cohen's d of 0.72, significantly reducing false alarms.
Insider threat detection is getting an increased concern from academia, industry, and governments due to the growing number of malicious insider incidents. The existing approaches proposed for detecting insider threats still have a common shortcoming, which is the high number of false alarms (false positives). The chal…
On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detectionstat.ML
Paper uses optimal transport-based statistics for change point detection.
problem Change point detection in multivariate data.
method Soft rank energy and entropically regularized optimal transport.
result Soft rank energy performs better in real datasets with strong continuity and convergence properties.
New test detects independence in streaming data, adapting to data complexity.
problem Independence testing in streaming data with adaptive stopping.
method Sequential kernelized independence tests using betting principles.
result Valid inference in streaming data with improved power.
New algorithm for adaptive experimental design in scientific settings.
problem Identifying true positives while controlling false discoveries in adaptive experimental design.
method Provably sample efficient adaptive algorithm for FDR control.
result First provably sample efficient adaptive algorithm for adaptive experimental design.
Online monitor detects classifier drift and adapts predictions.
problem Silent degradation of classifier accuracy under distributional shift.
method Sliding-window KS statistic with calibrated alarm thresholds.
result 86.6% valid detection across various shift conditions.
Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign variants (e.g., slowin…
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Gridcs.CR
Random Forest outperforms other IDS algorithms in smart grids.
problem Security vulnerabilities in smart grids.
method Comparison of four data mining algorithms (Random Forest, SVM, Neural Network, and KNN) in detecting attacks.
result Random Forest outperforms other algorithms in terms of detection accuracy and efficiency.
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.
MI attacks often mislabel non-training samples, making them impractical.
problem MI attacks mislabel non-training samples, leading to high false positive rate.
method Analyzed new features like distance to decision boundary and gradient norms.
result MI attacks cannot achieve high accuracy and low false positive rate simultaneously.
Paper improves voice trigger detection for privacy-centric smart assistants.
problem Mitigating false triggers in voice-activated smart assistants.
method Analyzing ASR lattices using graph neural networks (GNN).
result GNNs effectively reduce false triggers by ~87% at 99% true positive rate.
Survey of robust clustering methods for hotspot detection.
problem Detecting false positives in spatial hotspot mapping.
method Statistically rigorous clustering techniques.
result Survey of models and algorithms for robust clustering.
PITMonitor monitors model calibration over time with formal error guarantees.
problem Fixed-sample tests applied to models over time can lead to false alarms.
method PITMonitor uses mixture e-processes to detect distributional shifts in probability integral transforms.
result PITMonitor achieves competitive detection rates on river's FriedmanDrift benchmark.
New method enhances hotspot prediction in IC designs.
problem Design hotspots vary between designs and are hard to predict.
method Synthetic pattern generation based on DOEs.
result Significantly reduces false alarms in hotspot prediction.
Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored Projectionsstat.ME
A new method detects sparse changes in high-dimensional data streams using tailored PCA projections.
problem Detecting sparse changes in high-dimensional data streams.
method Tailored PCA projections for online change detection.
result High efficiency in detecting even very sparse changes in mean, variance, and correlation.