ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs
This paper investigates the use of the ASTD language for ensemble anomaly detection in data logs. It uses a sliding window technique for continuous learning in data streams, coupled with updating learning models upon the completion of each window to maintain accurate detection and align with current data trends. It proposes ASTD patterns for combining learning models, especially in the context of unsupervised learning, which is commonly used for data streams. To facilitate this, a new ASTD operator is proposed, the Quantified Flow, which enables the seamless combination of learning models while ensuring that the specification remains concise. Our contribution is a specification pattern, highlighting the capacity of ASTDs to abstract and modularize anomaly detection systems. The ASTD language provides a unique approach to develop data flow anomaly detection systems, grounded in the combination of processes through the graphical representation of the language operators. This simplifies the design task for developers, who can focus primarily on defining the functional operations that constitute the system.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FastDTW is approximate and Generally Slower than the Algorithm it Approximates
Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two de…
Anomaly DetectionClusteringDynamic Time WarpingTime Series+1Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
Anomaly detection in dynamic networks is critical for applications from cybersecurity to industrial monitoring, yet existing methods face challenges in energy efficiency, temporal precision, and adaptability. This paper …
Graph Anomaly DetectionFastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis
Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthes…
DenoisingGPUSpeech Synthesistext-to-speech+3TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this task, which learn point-wise representati…
Anomaly DetectionLarge Language ModelTime SeriesTime Series Anomaly DetectionCALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams
The detection of anomalies in non-stationary time-series streams is a critical but challenging task across numerous industrial and scientific domains. Traditional models, trained offline, suffer significant performance d…
Anomaly Detection