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Razberi Technologies is a developer and manufacturer of network video solutions for professional video surveillance and security applications. Providing ground-breaking solutions designed for simplicity and ease-of-use, the company’s flagship product is the patented razberi™ ServerSwitch™ which combines the functions of a network video recorder and Ethernet Smart Switch into a single compact appliance. Razberi Technologies is a privately-held corporation based in Farmers Branch, Texas, a part of the Dallas/Fort Worth metroplex.
Ocean Wave Networks is a San Diego, CA-based company in the Computers and Electronics sector.
Cyveillance, a world leader in cyber intelligence, provides an intelligence-led approach to security. Through continuous, comprehensive Internet monitoring and sophisticated intelligence analysis, Cyveillance proactively identifies and eliminates threats to information, infrastructure, individuals and their interactions, enabling its customers to preserve their reputation, revenues, and customer trust. Cyveillance serves the Global 2000 and OEM Data Partners — protecting the majority of the Fortune 50, regional financial institutions nationwide, and more than 100 million global consumers through its partnerships with security and service providers that include Blue Coat, AOL and Microsoft. Cyveillance is a wholly-owned subsidiary of QinetiQ, a FTSE250 company which uses its domain knowledge to provide technical support and know-how to customers in the global aerospace, defence and security markets.
Amerindia Technologies Inc is a Plainsboro, NJ-based company in the Computers and Electronics sector.
SignalSense exists to increase the fidelity of security alerts and expand on what can be detected in enterprise networks. The challenges facing those responsible for securing networks have increased exponentially in the last few years -- both from the sophistication of the threats seen, the explosion of agile development and the distribution of applications across varied environments. This is further compounded by the move to public cloud environments, where traditional inspection methods don’t exist. The challenges of delivering high fidelity security visibility into these environments will require new types of security tools that are just now being developed. Machine learning and neural networks, combined with new scalable methods of data collection and processing will provide the opportunity to keep up with the pace of growth in these new agile environments. The foundation of the SignalSense approach is an investment in data science that leverages a variety of machine learning methods, including deep learning, and GPU capacity to classify, characterize and profile network traffic. This enhances detection of threat and compromise, saving valuable investigation time for network security operators.