The paper proposes MaskDiff-AD, a forward-only masked diffusion model trained on nominal data to achieve state-of-the-art anomaly detection across various categorical, mixed-type, and text datasets.

ChronosAD introduces a novel architecture that uses time series foundation models and a custom Temporal Block to achieve robust and highly accurate anomaly detection across diverse domains.

The paper introduces a structured benchmark (TGAD) showing that current text-guided anomaly detection models often overstate their language conditioning, as performance significantly degrades when the…

The paper introduces VisAnomReasoner, a parameter-efficient Vision-Language Model (VLM), trained on a new benchmark (VisAnomBench) to accurately and interpretably detect anomalies in time-series data.

The paper proposes a system-aware unsupervised framework that combines lightweight online detection with a contextual digital twin and LLM to provide interpretable, actionable anomaly diagnoses for In…

This paper evaluates unsupervised temporal learning models, specifically recurrent autoencoders, for real-time anomaly detection in vulnerable IEC-61850 GOOSE networks, demonstrating that the GRU mode…

The paper proposes PROVFUSION, a multi-view fusion framework that integrates anomaly signals from attribute, structure, and causality views to overcome the limitations of single node- or edge-centric…

The paper proposes an ensemble learning framework combined with SHAP-based Explainable AI (XAI) to achieve robust and interpretable anomaly detection for network traffic in embedded systems.

IstGPT introduces a novel LLM-based framework for real-time, fine-grained anomaly detection in complex industrial cyber-physical systems, achieving state-of-the-art performance across multiple benchma…

This paper presents a fully unsupervised framework called CRAFTIIF for detecting four types of anomalies in multivariate time series data.

The paper proposes ANoCo, a training-free method that detects visual anomalies by quantifying how much a query patch deviates from the structure of a fixed normal feature manifold using graph Laplacia…

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

The paper proposes reframing mechanistic anomaly detection (MAD) as a functional attribution problem, using influence functions to measure how much a model's output depends on specific input samples,…

This paper enhances anomaly-based Intrusion Detection Systems by integrating process mining to provide detailed, process-based explanations and severity ratings for detected network anomalies.

GRASP introduces a novel graph-based anomaly detection system that uses masked self-supervised classification on process provenance graphs to robustly identify unknown and unknown-unknown anomalous be…

This paper enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

NLLog introduces a lightweight system that converts structured security logs into natural language sentences for improved anomaly detection, achieving high performance with low false-positive rates su…

NLLog is a lightweight pipeline that rewrites system-generated logs into natural language for improved analysis and comprehension.

The paper proposes using geometric metrics, specifically eigenspace alignment, to monitor the structural integrity of large behavioral populations, demonstrating its effectiveness in detecting network…

The paper evaluates AI's effectiveness in detecting network intrusions and cryptographic side-channel leakage, finding high accuracy in stable environments but performance degradation with novel traff…