Parallel and distributed computing for cybersecurity

The Minnesota Intrusion Detection System (MINDS), a data mining based system, can detect sophisticated cyberattacks on large-scale networks using signature-based systems. At MINDS' core is a behavioral-anomaly detection module based on a novel data-driven technique for calculating the distance between points in high-dimensional space, enabling meaningful calculation of the similarity between records containing a mixture of categorical and numerical attitudes. MINDS uses the shared nearest neighbor clustering algorithm, which works particularly well when data is high-dimensional and noisy. Its ability to summarize large amounts of network traffic can be highly valuable for network security analysts who must deal with large amounts of data.