Device communications generate ever growing amounts of network traffic metadata. Detecting malicious behavior attacks hidden within this data is essential to protecting business reputations, revenue, and opportunities. Malicious behaviors like advanced persistent threats, ransomware, modern zero-day attacks, trojans, vulnerabilities in IoT devices, or network misconfiguration often hide undetected in this traffic and strike when the network administrator is least prepared. Finding these attacks creates an enormous hurdle for IT security teams and IT security vendors.
GREYCORTEX MENDEL uses artificial intelligence techniques, machine learning, and advanced mathematical analysis to monitor network traffic, and create self-learned model of network behavior. This model adapts as traffic and threats in the network evolve, to effectively pinpoint malicious and anomalous behavior. Based on MENDEL's Advanced Security Network Matrics data, the model is able to identify subtle changes in network traffic caused by malicious actors. MENDEL quickly identifies security incidents, including correlating serious events spread across several different vectors, so that IT security teams can quickly take action to protect the network before damage happens.
GREYCORTEX its machine learning algorithms on identifying hidden threats within network traffic communication, which gives network professionals the security to know what is and isn’t hiding in their network. GREYCORTEX MENDEL’s advanced machine learning algorithms apply several additional analytic techniques to each flow, including the ability to presence of anomalous devices, communication volume, communication peers.