Abstract: During a network scanning, identifying the operating system (OS) running on each network attached host has been a research topic for a long time. Researchers have developed different approaches through network analysis using either passive or active techniques, such techniques are commonly called “OS fingerprinting”. According to best security practices, a set of security mechanisms should be applied to prevent OS fingerprinting by penetration testers. This article presents an experimental study to identify the parameters used by security controls to obfuscate their behavior on the network. A novel strategy is proposed to identify network devices despite static and dynamic obfuscation caused by security controls such as NAT, protocol scrubbers, or hardened systems. Targets were identified in virtual and native environments with a high degree of precisión, by means of a layered classification model integrated by K-means, KNN, Naive Bayes, SVM and ADA Boost classifiers.

We propose an improvement of a Monte Carlo method designed to treat the Ising model in a field [C. Lieu and J. Florencio, J. Low Temp. Phys. 89, 565 (1992)]. The method involves the counting of bonds linking neighboring like-spins and yields the degeneracy of the system's energy states, hence the partition function. There is no acceptance-rejection procedure and all the randomly generated configurations are kept. The sampling depends on geometry only, so results of a given run can be used for all temperatures and energy parameters. In order to understand the virtues and inadequacies of the method, we obtained exact results for small lattices. We find that a Monte Carlo run must be followed by a Gaussian fit in order to account properly for the rare events not recorded in the sampling. Finally, we also established bounds for the location of the peak for the specifc heat of the Ising model in a magnetic field in two dimensions for several values of the field in the thermodynamic limit.

We study the effect of random bonds and fields on the dynamical behavior of the one-dimensional transverse Ising model with four-spin interactions. We consider finite chains of increasing size to determine the time-dependent correlation function and the longitudinal relaxation function of the infinite chain. In this fully disordered system we observe a crossover from a collective modetype of dynamics to that of a central regime.