Wavelabs Insight framework can analyze and process fast-moving streams of data, and deliver real-time insights for mission-critical scenarios, thereby, significantly reducing the complexity and costs associated with building and training Machine Learning (ML) models for anomaly detection.
Wavelabs Insight can help build and train ML models to identify potential fraudulent activities faster, so you can avoid any online payment transactions before processing payments and fulfilling orders. The framework can also customize the models to cater to your own dataset, ensuring higher accuracy when compared to one-size-fits-all ML solutions.
Wavelabs Insight enables detection and classification of anomalies in a network by building ML models that can continuously monitor the events in computer systems and networks by processing large volumes of the data generated by these sources. Using a deep packet inspection that combines the functionality of an intrusion detection system (IDS) and an intrusion prevention system (IPS), these models can detect network anomalies and attacks, while still ensuring data security.
Wavelabs Insight ML models can detect unusual and erroneous readings to flag systems that are prone to failure, malfunction, rapid attrition, malicious attacks, theft, and tampering by processing big volumes of the data generated by IoT sensors. With this framework, we have built and compared the performances of several ML models to predict the anomalous behavior of IoT systems accurately. The ML algorithms primarily used are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), etc.
Wavelabs Insight also provides for dashboards to track efficiency, create predictive maintenance plans, and remotely monitor the real-time status of all systems.
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