Open research questions in Anomaly Detection Techniques and Applications

What the literature leaves open

• The paper identifies a gap in existing fairness-aware community detection methods, which can reinforce power asymmetries and demographic biases. The gap is due to the lack of consideration of proportional balance in fairness metrics.

  MOUFLON: multi-group modularity-based fairness-aware community detection · 2026 · DOI

• The need for real-time anomaly detection in smart homes while addressing these challenges. The lack of efficient and accurate anomaly detection models suitable for edge devices.

  Edge AI for Real-Time Anomaly Detection in Smart Homes · 2025 · DOI

• The current analysis methods are hindered by sampling limitations, temporal issues, and subjectivity. Traditional community detection algorithms have limitations in addressing temporal issues and subjectivity. There is a need for a scalable detection pipeline for identifying abnormal alliances in tax datasets.

  Abnormal Alliance Detection Method Based on a Dynamic Community Identification and Tracking Method for Time-Varying Bipartite Networks · 2025 · DOI

• Although the multilingual semantic–numerical collaborative sensing framework demonstrates stable performance improvements across multiple markets, several directions warrant further investigation.

  A Cross-Modal Temporal Alignment Framework for Artificial Intelligence-Driven Sensing in Multilingual Risk Monitoring · 2026 · DOI

• The paper does not discuss the limitations of the proposed HS-COS framework in detail. The experimental evaluation is limited to a few real-world datasets. The paper does not provide a comprehensive comparison with other state-of-the-art outlier detection techniques.

  Hybrid Hard–Soft Clustering For Outlier Detection: Development of the HS-COS Framework · 2026 · DOI

• Future research can focus on evaluating the proposed HS-COS framework on more real-world datasets. Future research can focus on comparing the proposed framework with other state-of-the-art outlier detection techniques. Future research can focus on improving the scalability of the proposed framework.

  Hybrid Hard–Soft Clustering For Outlier Detection: Development of the HS-COS Framework · 2026 · DOI

• Traditional industrial monitoring architectures rely on rigid, binary limits that trigger false alarms or fail to detect subtle degradations. The lack of integration of multidimensional statistical baselines and correlation coefficients in industrial monitoring.

  Machine Learning for Industrial Anomaly Detection using Bivariate Gaussian Probability Density Function: A Case Study · 2026 · DOI

• The gap is that training samples from the target domain are usually not available in surveillance scenarios. The gap is that current mainstream paradigms require abundant samples from the target video domain for training.

  Action hints: Semantic typicality and context uniqueness for generalizable skeleton-based video anomaly detection · 2026 · DOI

• The paper identifies the gap in existing anomaly detection methods when applied to target domains with limited data. It also identifies the need for a statistically rigorous framework for Autoencoder-based AD after Representation Learning-based DA.

  Statistical inference for autoencoder-based anomaly detection after representation learning-based domain adaptation · 2026 · DOI

• The cross-scale feature fusion module addresses the problems of feature redundancy as well as insufficient information expressiveness, and utilizes the anomaly map modulation operation to further enhance the sensitivity to anomaly regions.

  Knowledge-prior memory discrimination based generative adversarial network for industrial anomaly detection and localization · 2026 · DOI

• Improving anomaly sensitivity, particularly for rare and subtle events, remains an important direction for future research. Potential enhancements include adaptive thresholding strategies that can adjust to evolving system dynamics, as well as the integration of prediction-based components or latent-space regularization to improve feature separability.

  Deep Learning–Driven Anomaly Detection for IoT-Enabled Smart Engineering Systems · 2026 · DOI

• Insider threats are able to mimic normal operations, making them difficult to detect. The field's shift toward deep log-sequence modeling has led to a methodological gap, as statistical cues are weakened or lost. Detecting progressive, weak-signal threats is a challenging task.

  MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling · 2026

• The field's shift toward deep log-sequence modeling has led to a methodological gap, as statistical cues are weakened or lost. Earlier ITD systems relied on statistical features and temporal window analysis, making them naturally sensitive to irregularities.

  MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling · 2026

• Third, detection delay—the time elapsed between fault onset and reliable detection—has not been quantified in this work. These results suggest that, with limited data, architectural parsimony is more important than expressive capacity.

  UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction · 2026

• Mining anomalies from unmanned aerial vehicle (UAV) state-estimation logs is challenging because failures are sparse, temporally structured, and distributed across heterogeneous PX4 telemetry streams with variable sensor availability and missing values.

  AeroTSBoost: Temporal-Statistical Boosting for Real-World UAV Telemetry Anomaly Mining · 2026

• The evaluation was limited to post-cardiac surgical patients. The framework has restrictions on the selection of alerts.

  Conditional outlier detection for clinical alerting · 2026 · DOI

• The paper suggests extending the approach to the case where the mean squared error is used as the reconstruction error. It also suggests applying the approach to a broader class of piecewise linear networks.

  Statistical inference for autoencoder-based anomaly detection after representation learning-based domain adaptation · 2026 · DOI

• Collecting representative anomalous data remains challenging. Most existing approaches are primarily evaluated on benchmarks or in simulation environments.

  Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection · 2026

• Multi-objective optimisation frameworks can systematically characterise the trade-offs between accuracy, model size, latency, and energy consumption - Further research on dynamic PTQ…

  LightViT-AD: lightweight vision transformer distillation for unsupervised UAV anomaly detection with real-time edge inference · 2026 · DOI

• The applicability of the method in practice is often limited by strict data requirements - Semi-supervised methods usually depend on frame-level annotations for a subset of training samples - Unsupervised methods assume access to clean anomaly-free training…

  Dynamic feature screening network with consistency constraints for weakly supervised video anomaly detection · 2026 · DOI

• Investigating the use of other types of convolutional blocks - Exploring the application of DFS-Net to other domains - Further analysis of the impact of the feature similarity loss weight λ sim on the performance…

  Dynamic feature screening network with consistency constraints for weakly supervised video anomaly detection · 2026 · DOI

• Investigating the application of the proposed system to other types of anomaly detection, - Exploring the use of other types of feature extractors or convolutional networks, - Developing more advanced methods for dynamic weighting and residual-driven…

  Motion-guided local–global state prediction for 1-ms anomaly detection in fast-moving industrial scenarios · 2026 · DOI

• Conventional 60-fps video is often insufficient for describing fast-moving anomalous processes, - Existing methods are often too large and structurally complex for a fully streaming 1-ms FPGA…

  Motion-guided local–global state prediction for 1-ms anomaly detection in fast-moving industrial scenarios · 2026 · DOI

• Evaluating lightweight transformer architectures optimized for edge deployment - Exploring self-supervised learning - Investigating transformer-based detection - Deployment in real-world operational…

  Edge AI for Real-Time Anomaly Detection in Smart Homes · 2025 · DOI

• Limited to binary classification, - Relies on specific datasets, - No mention of real-world…

  Multi-Object Tracking and Spatio-Temporal Graph-Based Relational Pattern Mining · 2026 · DOI

Most-cited papers in Anomaly Detection Techniques and Applications

• Machine Learning: Algorithms, Real-World Applications and Research Directions · SN Computer Science · 2021 · 4,943 citations
• Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median · Journal of Experimental Social Psychology · 2013 · 3,551 citations
• A review of uncertainty quantification in deep learning: Techniques, applications and challenges · Information Fusion · 2021 · 2,587 citations
• Deep Learning for Anomaly Detection · ACM Computing Surveys · 2021 · 2,519 citations
• Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions · SN Computer Science · 2021 · 2,443 citations
• A survey on deep learning and its applications · Computer Science Review · 2021 · 1,413 citations
• TranAD · Proceedings of the VLDB Endowment · 2022 · 1,020 citations
• A Unifying Review of Deep and Shallow Anomaly Detection · Proceedings of the IEEE · 2021 · 933 citations
• A Review on Outlier/Anomaly Detection in Time Series Data · ACM Computing Surveys · 2021 · 897 citations
• Reconstruction by inpainting for visual anomaly detection · Pattern Recognition · 2020 · 744 citations

Most recent work

• Conditional outlier detection for clinical alerting · PubMed · 2026
• The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection · International Journal of Computer Vision · 2026
• Temporal–contextual self-supervised time-series learning for automated fault detection and diagnosis of air handling units in buildings · Building and Environment · 2026
• Deep Learning for Video Anomaly Detection: A Review · IEEE Transactions on Neural Networks and Learning Systems · 2026
• Angel or devil: Discriminating hard samples and anomaly contaminations for unsupervised time series anomaly detection · Neural Networks · 2026
• Causally Invariant Video Anomaly Detection via Counterfactual Reasoning and prototype intervention · Pattern Recognition · 2026
• Gated Memory-Guided Multi-scale spatio–temporal–spectral feature fusion network for unsupervised Internet of Things time series anomaly detection · Engineering Applications of Artificial Intelligence · 2026
• Not Only Detection But Also Explain: An Interpretable Multimodal System for Anomaly Detection · IEEE Transactions on Emerging Topics in Computational Intelligence · 2026
• Generalizing CLIP prompts for zero-shot anomaly detection · Pattern Recognition · 2026
• Conformal machine learning for reliable anomaly detection in industrial cyber-physical systems · Reliability Engineering & System Safety · 2026

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