Malware is a central and industry-wide challenge to Internet security. Currently, the state of the art malware detection engines are constructed through a manual intensive, inefficient, and slow process. Cyber 20/20 offers a revolutionary new automated design approach to malware detection by removing the human in the loop risk and time factors. Our platform leverages cutting-edge machine learning algorithms accelerated with high-performance computing to analyze large amounts of malware yielding an extremely high detection and low false positive rate. We have developed a targeted malware detection engine for the financial industry by training on a large data set of over 37 million unique malware variants attacking financial institutions.

Our OS-agnostic and fully automated platform is a highly scalable malware characterization and machine learning software framework. It leverages massive computational resources on the cloud and high-performance acceleration of machine learning algo...
Malware is a central and industry-wide challenge to Internet security. Currently, the state of the art malware detection engines are constructed through a manual intensive, inefficient, and slow process. Cyber 20/20 offers a revolutionary new automated design approach to malware detection by removing the human in the loop risk and time factors. Our platform leverages cutting-edge machine learning algorithms accelerated with high-performance computing to analyze large amounts of malware yielding an extremely high detection and low false positive rate. We have developed a targeted malware detection engine for the financial industry by training on a large data set of over 37 million unique malware variants attacking financial institutions.

Our OS-agnostic and fully automated platform is a highly scalable malware characterization and machine learning software framework. It leverages massive computational resources on the cloud and high-performance acceleration of machine learning algorithms through GPUs. Our platform processes and learns from hundreds of millions of different malware variants on a daily basis using a range of big data technologies, e.g., Spark, Amazon Elastic Map Reduce, HIVE, and MongoDB.
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Johnny Rutkowski
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Johnny Rutkowski