Optimally Identifying Worm-Infected Hosts

We have proposed a method of identifying superspreaders by flow sampling and a method of filtering legitimate hosts from the identified superspreaders using a white list. However, the problem of how to optimally set parameters of phi, the measurement period length, m*, the identification threshold of the flow count m within phi, and H*, the identification probability for hosts with m = m*, remained unsolved. These three parameters seriously impact the ability to identify the spread of infection. Our contributions in this work are two-fold: (1) we propose a method of optimally designing these three parameters to satisfy the condition that the ratio of the number of active worm-infected hosts divided by the number of all vulnerable hosts is bound. by a given upper-limit during the time T required to develop a patch or an anti-worm vaccine, and (2) the proposed method can optimize the identification accuracy of worm-infected hosts by maximally using a limited amount of memory resource of monitors.