DESIGNED FOR VALUE CREATION.
DESIGNED WITH THE USER IN MIND.

Our technology was built from the ground up with a very special purpose in mind: to move you up the value chain by leveraging your data, no matter its volume, variety or velocity, and without ever compromising on your customers' privacy and data protection.

Deep Packet Inspection


What is Deep Packet Inspection?

Deep packet inspection (DPI) is a type of network packet filtering. It evaluates both data and the header of a packet that is going through an inspection point. Simultaneously, it extracts data from packets.


How does DPI apply to threat detection?

DPI examines the content of the payload and extracts all the metadata that can be extracted. This information will be used by the threat detection engine to make accurate detection. This contextual information is also presented to the analyst on the dashboard so that the human analyst can have a better understanding of the threats and make informed decisions.

Intelligent Detection

With heuristics and behavioural based rule sets, DPI can provide intelligent security threat detection.

Advanced Detection

DPI provides the ability for full parsing of content layers of the packet which allows the detection of most dangerous attacks

Machine Learning


What is machine learning?

Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.


How is ML/DL applied to threat detection?

Creating rules for detecting cyberattacks is a traditional method of defence. However, due to the sheer volume of attacks and their complexity, automating detection through ML/DL is necessary for scaling up our defences. ML/DL algorithms help us learn from data in order to detect similar attacks and respond to behaviour changes. They are applied in a series of detections such as traffic classification, malware domain classification (DGA), abnormal behaviour detection, tunnelling and exfiltration activities detection.

Product Scalability

Manual rule creation cannot scale for all threats. ML/DL can help close the detection gap by learning from data.

Advanced Detection

Based on semi-supervised and unsupervised methods to detect similar or changing attacks