Online unsupervised anomaly detection

A computerized-method for real-time detection of anomalous data, by processing high-speed streaming data, said computerized-method comprising:

• in a computerized-system comprising a processor and a memory, receiving by the processor, a data stream comprised of unlabeled data points,
• operating by the processor an Anomalous Data Detection (ADD) module, said ADD module is configured to:
• (i) receive:
• k, X, d, threshold, and n,
• wherein k is a number of data point neighbors for each data point,
• wherein X is a number of data points in a predetermined period of time,
• wherein d is a number of dimensions of each data point,
• wherein n is a number of data points that said ADD module is operating on, in
• a predefined time unit;
• (ii) prepare a dataset having n data points from the received X data points;
• and
• (iii) identify one or more data points, from the received data stream, as outliers to send an alert with details related to the identified outliers thus, dynamically evaluating local outliers in the received data stream.