• MILA

  • Referenced in 10 articles [sw02726]
  • MILA is flexible and efficient in detecting anomalies and novel patterns...
  • Dlib-ml

  • Referenced in 10 articles [sw14413]
  • based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy...
  • EFDR

  • Referenced in 8 articles [sw11106]
  • Rate (EFDR) is a tool to detect anomalies in an image. The image is first...
  • Colibri

  • Referenced in 7 articles [sw12043]
  • finding patterns (such as communities) and detecting anomalies. Additionally, it is desirable to track...
  • GigaTensor

  • Referenced in 5 articles [sw23741]
  • applications including clustering, trend detection, and anomaly detection. However, current tensor decomposition algorithms...
  • FRaC

  • Referenced in 3 articles [sw15627]
  • FRaC (Feature Regression and Classification) Anomaly Detection Algorithm. FRaC is a new general approach ... anomaly detection problem; that is, the task of identifying instances that come from a different ... distribution than the majority (unsupervised anomaly detection) or a set of verified “normal” data (semi ... supervised anomaly detection). Traditional approaches typically compare the position of a new data point...
  • scanstatistics

  • Referenced in 3 articles [sw22348]
  • package scanstatistics: Space-Time Anomaly Detection using Scan Statistics. Detection of anomalous space-time clusters ... data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte...
  • NetMine

  • Referenced in 2 articles [sw02020]
  • association rule extraction to profile communications, detect anomalies, and identify recurrent patterns. Association rule extraction...
  • LeSiNN

  • Referenced in 1 article [sw20748]
  • LeSiNN: Detecting anomalies by identifying least similar nearest neighbours. We introduce the concept of Least ... Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method ... state-of-the-art anomaly detectors in terms of detection accuracy and runtime...
  • Loda

  • Referenced in 1 article [sw15626]
  • methods. In unsupervised learning, namely in anomaly detection such a paradigm has not yet been ... weak detectors can lead to a strong anomaly detector with a performance equal ... questions regarding batch-vs-on-line anomaly detection...
  • trendsegmentR

  • Referenced in 1 article [sw29264]
  • trendsegmentR: Linear Trend Segmentation and Point Anomaly Detection. Performs the detection of point anomalies...
  • Andromaly

  • Referenced in 1 article [sw23485]
  • proposed framework realizes a Host-based Malware Detection System that continuously monitors various features ... mobile device and then applies Machine Learning anomaly detectors to classify the collected data ... applications, and evaluated Andromaly’s ability to detect new malware based on samples of known ... malware. We evaluated several combinations of anomaly detection algorithms, feature selection method and the number...
  • SensGru

  • Referenced in 1 article [sw28022]
  • using sensgru. In the context of anomaly detection in cyber physical systems (CPS), spatiotemporal correlations...
  • VolTime

  • Referenced in 1 article [sw30720]
  • VolTime: unsupervised anomaly detection on users’ online activity volume...
  • kmodR

  • Referenced in 1 article [sw27460]
  • unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13 ... thesis, ”Clustering and anomaly detection in tropical cyclones”. Useful for creating (potentially) tighter clusters than...
  • Anomagram

  • Referenced in 1 article [sw34396]
  • evaluating autoencoders on the task of anomaly detection. Anomagram: Interactive Visualization for Autoencoders with Tensorflow.js...
  • GiViP

  • Referenced in 1 article [sw27736]
  • take advantage of GiViP to detect anomalies related to the computation and to the infrastructure...
  • VxInsight

  • Referenced in 2 articles [sw26293]
  • quantities of information, and at detecting patterns and anomalies. The challenge is to present...
  • NFIDS

  • Referenced in 1 article [sw34890]
  • detection system (NFIDS) is an anomaly based intrusion detection system that uses fuzzy logic...
  • Lumos

  • Referenced in 1 article [sw33645]
  • reject 1000s of false alarms detected by anomaly detectors. The application of Lumos has resulted...