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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.
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