Understanding human mobility patterns is crucial to fields such as urban mobility and mobile netw... more Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are twofold: first, we show significant similarities in people's mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individual's urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover people's tendency to revisit few favorite venues using the shortest-path available.
... Measure results of exploit executions in testing lab 748 virtual machines in Core's te... more ... Measure results of exploit executions in testing lab 748 virtual machines in Core's testing lab different OS and installed applications all the exploits are executed every night ... anonymized feedback program in Impact sensitive data is filtered out before sending it ...
Assessing network security is a complex and difficult task. Attack graphs have been proposed as a... more Assessing network security is a complex and difficult task. Attack graphs have been proposed as a tool to help network administrators understand the potential weaknesses of their network. However, a problem has not yet been addressed by previous work on this subject; namely, how to actually execute and validate the attack paths resulting from the analysis of the attack graph. In this paper we present a complete PDDL representation of an attack model, and an implementation that integrates a planner into a penetration testing tool. This allows to automatically generate attack paths for penetration testing scenarios, and to validate these attacks by executing the corresponding actions -including exploits- against the real target network. We present an algorithm for transforming the information present in the penetration testing tool to the planning domain, and show how the scalability issues of attack graphs can be solved using current planners. We include an analysis of the performance of our solution, showing how our model scales to medium-sized networks and the number of actions available in current penetration testing tools.
Understanding human mobility patterns is crucial to fields such as urban mobility and mobile netw... more Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are twofold: first, we show significant similarities in people's mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individual's urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover people's tendency to revisit few favorite venues using the shortest-path available.
... Measure results of exploit executions in testing lab 748 virtual machines in Core's te... more ... Measure results of exploit executions in testing lab 748 virtual machines in Core's testing lab different OS and installed applications all the exploits are executed every night ... anonymized feedback program in Impact sensitive data is filtered out before sending it ...
Assessing network security is a complex and difficult task. Attack graphs have been proposed as a... more Assessing network security is a complex and difficult task. Attack graphs have been proposed as a tool to help network administrators understand the potential weaknesses of their network. However, a problem has not yet been addressed by previous work on this subject; namely, how to actually execute and validate the attack paths resulting from the analysis of the attack graph. In this paper we present a complete PDDL representation of an attack model, and an implementation that integrates a planner into a penetration testing tool. This allows to automatically generate attack paths for penetration testing scenarios, and to validate these attacks by executing the corresponding actions -including exploits- against the real target network. We present an algorithm for transforming the information present in the penetration testing tool to the planning domain, and show how the scalability issues of attack graphs can be solved using current planners. We include an analysis of the performance of our solution, showing how our model scales to medium-sized networks and the number of actions available in current penetration testing tools.
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