Within Homeland Defense, a crucial aspect is related to Critical Infrastructure (CI) protection. In fact, CIs encompass a wide range of strategic sectors for countries, such as food, water, public health, emergency services, energy, transportation, information technology, telecommunication and finance. Therefore, CIs operation should be granted to ensure national needs satisfaction. In order to monitor and prevent dangerous situations and threats that could affect CIs, Situation Awareness (SAW) theory addresses the goal of maintaining operator awareness, through rough data acquired by heterogeneous sensors monitoring CIs. One of the great problems in SAW is related to the definition of agile models able to highlight threats and to adapt themselves to monitoring requirements. This paper describes how Data Mining (DM) approach can be applied on data acquired by sensors watching at infrastructures, in order to build agile Hidden Markov Models (HMMs), for on-going situation assessment and consequent threat evaluation.
Digioia, G., Panzieri, S. (2012). CRITICAL INFRASTRUCTURE PROTECTION: THREATS MINING AND ASSESSMENT. In Proceedings of the International Defense and Homeland Security Simulation Workshop 2012.
CRITICAL INFRASTRUCTURE PROTECTION: THREATS MINING AND ASSESSMENT
PANZIERI, Stefano
2012-01-01
Abstract
Within Homeland Defense, a crucial aspect is related to Critical Infrastructure (CI) protection. In fact, CIs encompass a wide range of strategic sectors for countries, such as food, water, public health, emergency services, energy, transportation, information technology, telecommunication and finance. Therefore, CIs operation should be granted to ensure national needs satisfaction. In order to monitor and prevent dangerous situations and threats that could affect CIs, Situation Awareness (SAW) theory addresses the goal of maintaining operator awareness, through rough data acquired by heterogeneous sensors monitoring CIs. One of the great problems in SAW is related to the definition of agile models able to highlight threats and to adapt themselves to monitoring requirements. This paper describes how Data Mining (DM) approach can be applied on data acquired by sensors watching at infrastructures, in order to build agile Hidden Markov Models (HMMs), for on-going situation assessment and consequent threat evaluation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
