Anomaly Detection’s Quantum Leap: From IoT Security to Brain Imaging and Beyond

Latest 50 papers on anomaly detection: Dec. 7, 2025

Anomaly detection is the bedrock of robust AI/ML systems, crucial for everything from cybersecurity to medical diagnostics. In our increasingly complex, data-rich world, pinpointing the unusual – be it a cyberattack, a faulty sensor reading, or a subtle medical symptom – is a constant challenge. Fortunately, recent research is pushing the boundaries, delivering ingenious solutions that are more precise, private, and powerful than ever before.### The Big Idea(s) & Core Innovationslatest advancements reveal a fascinating convergence of cutting-edge AI techniques: quantum computing, federated learning, and large language models (LLMs) are redefining what’s possible. Consider the bold step taken by independent researchers Davut Emre Taşar and Ceren Öcal Taşar with TARA Test-by-Adaptive-Ranks for Quantum Anomaly Detection with Conformal Prediction Guarantees. They’ve proven that conformal prediction remains valid for quantum data, providing robust, distribution-free guarantees for quantum anomaly detection – a critical step for securing quantum communication protocols. Complementing this, N. Wiebe et al. from the University of Toronto, in Opportunities and Challenges for Data Quality in the Era of Quantum Computing, highlight how quantum computing can revolutionize data quality management, particularly for anomaly detection and error correction. This is further echoed by Author One and Author Two from the Institute of Advanced Computing and Department of Electrical Engineering in their paper, Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection, introducing a hybrid quantum autoencoder for subtle anomaly detection in IoT.another front, the need for privacy and decentralized intelligence is driving the adoption of federated learning. Author A et al. from the University of Maritime Science in Federated Learning for Anomaly Detection in Maritime Movement Data showcase how this approach enables collaborative anomaly detection in sensitive maritime data without centralizing information. This theme is reinforced by Hao Zhang et al. from the University of Science and Technology of China, whose Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks improves detection of rare anomalies in resource-constrained IoT environments, demonstrating privacy-preserving resilience. Similarly, Author A and Author B from Maritime Informatics Lab and Robotics Institute, respectively, integrate federated learning with trajectory compression in Federated Learning and Trajectory Compression for Enhanced AIS Coverage for efficient, private maritime monitoring.transformative power of Large Language Models (LLMs) is also being unleashed for anomaly detection. Zhongyuan Wu et al. from Beihang University introduce ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models, a “train-once, apply-broadly” framework that redefines anomaly detection as contextual dissimilarity, generalizing across diverse modalities and domains. This vision is expanded by Wang et al. in their Evaluation of Large Language Models for Numeric Anomaly Detection in Power Systems, proving LLMs can outperform traditional methods in energy grid monitoring, crucial for grid reliability.these overarching themes, specialized innovations are making significant strides:Jungi Lee et al. from ELROILAB Inc. tackle contaminated training data with Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data (AAR), dynamically excluding anomalies for improved performance.For computer vision, Author A and Author B introduce CoDeGraph in On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection, a graph-based framework to detect and filter “consistent anomalies” in zero-shot settings, identifying issues like Similarity Scaling and Neighbor-Burnout.In medical imaging, Qinyi Cao et al. from The University of Sydney present ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rays, generating anatomically consistent anomalies for improved unsupervised detection in chest X-rays. Alexander Frotschera et al. explore deep unsupervised anomaly detection in brain imaging, highlighting biases in their Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis and suggesting algorithmic limitations over data scarcity.### Under the Hood: Models, Datasets, & Benchmarksbreakthroughs are often powered by novel architectures, meticulously curated datasets, and robust benchmarking strategies. Here are some key resources and methodologies:Quantum Models: Hybrid quantum-classical models like the quantum autoencoder trainable kernel (Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection) and quantum support vector machines with wavelet transformation (Modeling Wavelet Transformed Quantum Support Vector for Network Intrusion Detection) are emerging. Neural Architecture Search (NAS) is also being adapted for Quantum Autoencoders, as explored by Alice Johnson and Bob Smith (Neural Architecture Search for Quantum Autoencoders, code: https://github.com/quantum-ai/nas-for-qa), optimizing quantum circuit design.Federated Learning Frameworks: New federated learning (FL) architectures, often combined with semi-supervised and semi-asynchronous techniques, are designed for distributed, privacy-sensitive data common in IoT and maritime contexts (Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks).LLM-based Approaches: ICAD-LLM (ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models, code: https://github.com/nobody384/ICAD-LLM) leverages LLMs for multi-modal anomaly detection, reframing the problem as contextual dissimilarity. Another promising LLM application is demonstrated for numeric anomaly detection in power systems (Evaluation of Large Language Models for Numeric Anomaly Detection in Power Systems).Visual Anomaly Detection Models: Frameworks like ALARM (ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification, code: https://github.com/wyze-labs/ALARM) use MLLMs with uncertainty quantification. Concept Bottleneck Models are also being integrated for explainable visual anomaly detection (Explainable Visual Anomaly Detection via Concept Bottleneck Models, code: https://github.com/ConceptBottleneckModels/VisualAnomalyDetection). For few-shot learning, ABounD (ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection, code: https://github.com/ABounD) combines Dynamic Concept Fusion and Adversarial Boundary Forging.Time Series & Graph-based Models: Temporal Graph Neural Networks (TGNNs) are improving PV system monitoring (Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data). For microservices, FC-ADL (FC-ADL: Efficient Microservice Anomaly Detection and Localisation Through Functional Connectivity, code: https://github.com/FC-ADL/FC-ADL-SoCC) uses functional connectivity. In edge streams, ARES (ARES: Anomaly Recognition Model For Edge Streams, code: https://anonymous.4open.science/r/ARES-4573) combines GNNs with Half-Space Trees.Datasets & Benchmarks: New, comprehensive datasets are vital. ADNet (ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories) offers 380 categories for cross-domain evaluation. For video, Pistachio (Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks) provides synthetic, long-form, and balanced videos, while A2Seek (A2Seek: Towards Reasoning-Centric Benchmark for Aerial Anomaly Understanding, code: https://2-mo.github.io/A2Seek/) targets aerial anomaly understanding with reasoning. ClimaOoD (ClimaOoD: Improving Anomaly Segmentation via Physically Realistic Synthetic Data) creates physically realistic synthetic data for autonomous driving anomaly segmentation. Benchmarking for deep UAD in brain MRI is provided by Alexander Frotschera et al. (Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis, code: https://github.com/AlexanderFrotscher/UAD-IMAG).Cybersecurity Testbeds: The METL testbed is used by Benjamin Blakely et al. from Argonne National Laboratory in their AI-Driven Cybersecurity Testbed for Nuclear Infrastructure: Comprehensive Evaluation Using METL Operational Data for evaluating AI approaches in nuclear infrastructure cybersecurity, identifying change point detection as a top performer.### Impact & The Road Aheadinnovations are not just theoretical curiosities; they promise profound real-world impact. Enhanced anomaly detection means more secure IoT devices (e.g., A Novel Trust-Based DDoS Cyberattack Detection Model for Smart Business Environments), more resilient smart grids (An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids), and more reliable automated systems (e.g., I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation, code: https://github.com/LucasStill/I-GLIDE). The advent of explainable AI in anomaly detection, as seen with concept bottleneck models, builds trust in critical domains like medical diagnostics and industrial inspection. Privacy-preserving techniques like federated learning ensure that these advancements don’t come at the cost of sensitive data.road ahead involves refining these hybrid approaches, improving generalization across increasingly diverse and complex data, and addressing the nuanced challenges of concept drift (e.g., Neighborhood Density Estimation Using Space-Partitioning Based Hashing Schemes introducing Enhash for this). Moreover, the burgeoning field of quantum computing presents both opportunities and challenges for data quality and robust anomaly detection, promising a new era of ultra-secure and powerful AI systems. The ability to detect subtle, unseen, and sophisticated anomalies – often in real-time and with minimal labeled data – will be paramount in safeguarding our interconnected world and pushing the frontiers of intelligent automation. The future of anomaly detection is dynamic, distributed, and incredibly exciting.