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This paper investigates three aspects: a network vulnerability as the non-uniform vulnerable-host distribution, threats, i.e., intelligent malwares that exploit such a vulnerability, and defense, i.e., challenges for fighting the threats. They first study five large data sets and observe consistent clustered vulnerable-host distributions. They then present a new metric, referred to as the non-uniformity factor, which quantifies the unevenness of a vulnerable-host distribution. This metric is essentially the Renyi information entropy and better characterizes the non-uniformity of a distribution than the Shannon entropy. Next, they analyze the propagation speed of network-aware malwares in view of information theory. In particular, they draw a relationship between Renyi entropies and randomized epidemic malware-scanning algorithms.
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