Network Traffic Analysis Solution Identifies Compromised Devices Using ML/AI on Contextual NetFlow and Packet Inspection Data
Gurucul, a leader in behavior based security and fraud analytics technology for on-premises and the cloud, announced the Gurucul Network Behavior Analytics (NBA) solution, the industry’s most advanced Network Traffic Analysis product. It leverages Gurucul’s advanced machine learning analytics to provide identification of advanced and unknown cyber threats. Gurucul is exhibiting this technology next week in booth #1100 at BlackHat USA 2019.
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The Gurucul Network Behavior Analytics solution delivers flexible entity modeling to monitor and identify unusual, risky behavior from any entity. This includes traditional devices like workstations, servers and firewalls, as well as extended network devices such as Robotic Process Automation (RPA) processes, IoT devices (CCTV, vending machines), OT infrastructure (automation sensors used in manufacturing and utility industries) and point of sale (POS) devices.Most organizations tend to rely on network monitoring tools for checking the health of the network. These tools detect and report failures of devices or connections. However, they cannot repair problems, nor can they find unknown threats. By applying behavioral analysis to network traffic, a network traffic analysis solution can help organizations identify suspicious activities that conventional cybersecurity tools would overlook.
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“The adoption of cloud, mobile and IoT technologies is creating a much larger attack surface, while exposing organizations to entirely new categories of security threats including malicious bots and scripts,” said Nilesh Dherange, Chief Technology Officer for Gurucul. “As a result, addressing entity-based security threats in the network has become imperative. With very few inherent means to monitor devices and their behaviors, Gurucul’s network traffic analysis technology provides valuable detection, risk-scoring and alerting capabilities to preempt malicious activity.”
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