Inferring Internet Worm Temporal Characteristics

Internet worm attacks pose a significant threat to network security. In this work, we coin the term Internet worm tomography as inferring the characteristics of Internet worms from the observations of Darknet or network telescopes that are routable but unused IP addresses. Under the framework of Internet worm tomography, we attempt to infer worm temporal behaviors such as the host infection time and the worm infection sequence, and thus pinpoint patient zero. Specifically, we introduce statistical estimation techniques and propose method of moments, maximum likelihood, and linear regression estimators. We show analytically and empirically that our proposed estimators can better infer worm temporal characteristics than a naive estimator that has been used in the previous work

HOST AND NETWORK OPTIMIZATIONS FOR PERFORMANCE ENHANCEMENT AND ENERGY EFFICIENCY IN DATA CENTER NETWORKS

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