Wireless researchers face the practical challenge of evaluating the performance of new routing protocols for very large-scale heterogeneous mobile systems. While simulation platforms, tools and techniques have matured, doubts remain about the broad validity of simulated results when applied to real world situations. Wireless network emulation provides a middle ground between simulations and real-world testing. However, emulation environments are complex to run and cost-prohibitive for larger-scale tests. In order to maximize fidelity while managing complexity and costs, we developed a common framework that unifies simulation-based analysis and emulation experiment tools, in order to produce calibrated and scalable performance evaluations.

Polymorphic malcode remains a troubling threat. The ability formal code to automatically transform into semantically equivalent variants frustrates attempts to rapidly construct a single, simple, easily verifiable representation. We present a quantitative analysis of the strengths and limitations of shellcode polymorphism and consider its impact on current intrusion detection practice.

A method is proposed for semiparametric estimation where parametric and non- parametric criteria are exploited in density estimation and unsupervised learning. This is accomplished by making sampling assumptions on a dataset that smoothly interpolate between the extreme of independently distributed (or id) sample data (as in nonparametric kernel density estimators) to the extreme of independent identically distributed (or iid) sample data. This article makes independent simi- larly distributed (or isd) sampling assumptions and interpolates between these two using a scalar parameter. The parameter controls a Bhattacharyya affinity penalty between pairs of distributions on samples. Surprisingly, the isd method maintains certain consistency and unimodality properties akin to maximum likelihood esti- mation. The proposed isd scheme is an alternative for handling nonstationarity in data without making drastic hidden variable assumptions which often make esti- mation difficult and laden with local optima. Experiments in density estimation on a variety of datasets confirm the value of isd over iid estimation, id estimation and mixture modeling.