Clustering and Non-Gaussian Behavior in Granular Matter

We investigate the properties of a model of granular matter consisting of $N$ Brownian particles on a line subject to inelastic mutual collisions. This model displays a genuine thermodynamic limit for the mean values of the energy and the energy dissipation. When the typical relaxation time $\tau$ associated with the Brownian process is small compared with the mean collision time $\tau_c$ the spatial density is nearly homogeneous and the velocity probability distribution is gaussian. In the opposite limit $\tau \gg \tau_c$ one has strong spatial clustering, with a fractal distribution of particles, and the velocity probability distribution strongly deviates from the gaussian one.Comment: 4 pages including 3 eps figures, LaTex, added references, corrected typos, minimally changed contents and abstract, to published in Phys.Rev.Lett. (tentatively on 28th of October, 1998

Bifurcations of a driven granular system under gravity

Molecular dynamics study on the granular bifurcation in a simple model is presented. The model consists of hard disks, which undergo inelastic collisions; the system is under the uniform external gravity and is driven by the heat bath. The competition between the two effects, namely, the gravitational force and the heat bath, is carefully studied. We found that the system shows three phases, namely, the condensed phase, locally fluidized phase, and granular turbulent phase, upon increasing the external control parameter. We conclude that the transition from the condensed phase to the locally fluidized phase is distinguished by the existence of fluidized holes, and the transition from the locally fluidized phase to the granular turbulent phase is understood by the destabilization transition of the fluidized holes due to mutual interference.Comment: 35 pages, 17 figures, to be published in PR

Automatic Graph-Based clustering for security logs

Computer security events are recorded in several log files. It is necessary to cluster these logs to discover security threats, detect anomalies, or identify a particular error. A problem arises when large quantities of security log data need to be checked as existing tools do not provide sufficiently sophisticated grouping results. In addition, existing methods need user input parameters and it is not trivial to find optimal values for these. Therefore, we propose a method for the automatic clustering of security logs. First, we present a new graph-theoretic approach for security log clustering based on maximal clique percolation. Second, we add an intensity threshold to the obtained maximal clique to consider the edge weight before proceeds to the percolations. Third, we use the simulated annealing algorithm to optimize the number of percolations and intensity threshold for maximal clique percolation. The entire process is automatic and does not need any user input. Experimental results on various real-world datasets show that the proposed method achieves superior clustering results compared to other methods