Know The Benefits Of Anomaly Detection

Anomaly detection helps in identifying the data items, points, events or observations, which usually do not match the expected prototype of a particular group. Though the anomalies occur rarely, they may indicate a huge and major threat such as cyber fraud and intrusions.

Anomaly detection is the process of detecting outliers in a database and assisting in taking corrective actions. Examples include identifying faulty goods in manufacturing, detecting fraud in financial transactions, monitoring equipments in a big server network, etc.

Other benefits of Anomaly detection are:

Spark anomaly detection helps in detecting and acting on anomalies related to streaming data. They ensure faster and accurate result and provide with the required signals and indications to the organisation to help avoid fraud.

Big data anomaly detection is greatly used in behavioural analysis and observation along with other types of analysis for assisting in gaining knowledge about the detection, recognition and forecast of the occurrence and reasons of these anomalies.

Also known as Outlier detection, the methods for anomaly detection are: o Replicator neural networkso Distance-based techniqueso Determination of records that deviate from learned association ruleso One-class support vector machineso Cluster analysis-based anomaly detection

Timely Detection of anomalous prototypes in data can help in taking significant actionable approaches in a wide range of application domains, including energy monitoring, fraud detection, predictive healthcare, network traffic management and many more.

Spark anomaly detection also helps in predicting mechanical failures, presenting the right deal at the right time, presenting with predictive healthcare and making decisions confidently in real-time.

Though anomaly detection with accuracy can be a complicated task, it is highly critical in today’s competitive and unpredictable business atmosphere. Since anomaly continuously fluctuates and anomalous patterns are also unexpected, an efficient and competent anomaly detection system must constantly self-learn and develop without much depending on pre-programmed or automated thresholds.

Big data anomaly detection can prove to be highly effective as it is the data-mining process and helps in determining the categories and nature of anomalies that occur in a particular data set. It also helps in determining the aspects of their occurrence, their intensity and the result it may produce. It is the most appropriate in domains, including fault detection, fraud identification, intrusion detection, event detection process in sensor networks and system health monitoring.

As far as intrusion and fraud detection are concerned, the anomalies are not essentially the uncommon items but are unforeseen bursts of actions. Hence, these anomalies do not match to the explanation of outliers or anomalies as rare incidents. While various anomaly detection methods may not work in these cases, big data and spark anomaly detection would effectively work if appropriately trained.