Abstract: An analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g.

Abstract: An analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g.

Abstract: An analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g.

Abstract: An analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g.

Abstract: An analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g.

Abstract: An analysis system automates IP address structure discovery by deep analysis of sample IPv6 addresses using a set of computational methods, namely, information-theoretic analysis, machine learning, and statistical modeling. The system receives a sample set of IP addresses, computes entropies, discovers and mines address segments, builds a network model of address segment inter-dependencies, and provides a graphical display with various plots and tools to enable a network analyst to navigate and explore the exposed IPv6 address structure. The structural information is then applied as input to applications that include: (a) identifying homogeneous groups of client addresses, e.g., to assist in mapping clients to content in a CDN; (b) supporting network situational awareness efforts, e.g., in cyber defense; (c) selecting candidate targets for active measurements, e.g.