A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults

• URL: http://arxiv.org/abs/2310.06779v1
• Date: Tue, 10 Oct 2023 16:54:25 GMT
• Title: A Supervised Embedding and Clustering Anomaly Detection method for classification of Mobile Network Faults
• Authors: R. Mosayebi, H. Kia, A. Kianpour Raki
• Abstract summary: The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD) It is a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring. SEMC-AD achieves 99% anomaly detection, whereas random forest and XGBoost only detect 86% and 81% of anomalies, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD), a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring caused by the growing volume of alarm logs. SEMC-AD employs a supervised embedding approach based on deep neural networks, utilizing historical alarm logs and their labels to extract numerical representations for each log, effectively addressing the issue of imbalanced classification due to a small proportion of anomalies in the dataset without employing one-hot encoding. The robustness of the embedding is evaluated by plotting the two most significant principle components of the embedded alarm logs, revealing that anomalies form distinct clusters with similar embeddings. Multivariate normal Gaussian clustering is then applied to these components, identifying clusters with a high ratio of anomalies to normal alarms (above 90%) and labeling them as the anomaly group. To classify new alarm logs, we check if their embedded vectors' two most significant principle components fall within the anomaly-labeled clusters. If so, the log is classified as an anomaly. Performance evaluation demonstrates that SEMC-AD outperforms conventional random forest and gradient boosting methods without embedding. SEMC-AD achieves 99% anomaly detection, whereas random forest and XGBoost only detect 86% and 81% of anomalies, respectively. While supervised classification methods may excel in labeled datasets, the results demonstrate that SEMC-AD is more efficient in classifying anomalies in datasets with numerous categorical features, significantly enhancing anomaly detection, reducing operator burden, and improving network maintenance.

Related papers

• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Open-Set Graph Anomaly Detection via Normal Structure Regularisation [30.638274744518682]
  Open-set Graph Anomaly Detection (GAD) aims to train a detection model using a small number of normal and anomaly nodes. Current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes. We propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to achieve generalised detection ability to unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-12T13:25:28Z)
• A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection [4.494911384096143]
  Detection of anomalous events in videos is an important problem in applications such as surveillance. We propose a simple-but-effective two-stage pseudo-label generation framework that produces segment-level (normal/anomaly) pseudo-labels. The proposed coarse-to-fine pseudo-label generator employs carefully-designed hierarchical divisive clustering and statistical hypothesis testing.
  arXiv  Detail & Related papers  (2023-10-26T17:59:19Z)
• MSFlow: Multi-Scale Flow-based Framework for Unsupervised Anomaly Detection [124.52227588930543]
  Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications. An inconspicuous yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection and localization in an unsupervised fashion. We propose a novel Multi-Scale Flow-based framework dubbed MSFlow composed of asymmetrical parallel flows followed by a fusion flow. Our MSFlow achieves a new state-of-the-art with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8%, and PRO score of 97.1%.
  arXiv  Detail & Related papers  (2023-08-29T13:38:35Z)
• RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision [21.393509817509464]
  This paper proposes a novel semi-supervised anomaly detection method, which devises textitcontamination-resilient continuous supervisory signals Our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR.
  arXiv  Detail & Related papers  (2023-07-25T04:04:49Z)
• Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos [20.368114998124295]
  We propose a weakly supervised anomaly detection system which has multiple contributions including a random batch selection mechanism. A clustering loss block is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. Extensive analysis of the proposed approach is provided using three popular anomaly detection datasets.
  arXiv  Detail & Related papers  (2022-03-25T15:18:19Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• Self-Trained One-class Classification for Unsupervised Anomaly Detection [56.35424872736276]
  Anomaly detection (AD) has various applications across domains, from manufacturing to healthcare. In this work, we focus on unsupervised AD problems whose entire training data are unlabeled and may contain both normal and anomalous samples. To tackle this problem, we build a robust one-class classification framework via data refinement. We show that our method outperforms state-of-the-art one-class classification method by 6.3 AUC and 12.5 average precision.
  arXiv  Detail & Related papers  (2021-06-11T01:36:08Z)
• CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection [20.368114998124295]
  We propose a weakly supervised anomaly detection method which has manifold contributions. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively.
  arXiv  Detail & Related papers  (2020-11-24T13:27:40Z)
• Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs [59.04636530383049]
  We propose Logsy, a classification-based method to learn log representations. We show an average improvement of 0.25 in the F1 score, compared to the previous methods.
  arXiv  Detail & Related papers  (2020-08-21T07:26:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.