Simulation-based study of botnets and defense mechanisms against them

To defend oneself against botnet attacks, one must have tools that make it possible to investigate the processes occurring on all stages of the lifecycle of botnets (propagation, control, attack) and possess defense mechanisms that can counteract botnets. A simulation-based approach to the investigation of botnets and the corresponding defense mechanisms is proposed. The simulation is performed using a special software environment developed by the authors. The architecture of this environment and the libraries needed to create models of botnets and defense mechanisms are described. Experimental data demonstrating the capabilities of the simulation environment for studying various stages of the botnet lifecycle and the efficiency of the corresponding defense mechanisms are discussed.     

A formally based parallelization of data mining algorithms for multi-core systems

We describe a novel, systematic approach to efficiently parallelizing data mining algorithms: starting with the representation of an algorithm as a sequential composition of functions, we formally transform it into a parallel form using higher-order functions for specifying parallelism. We implement the approach as an extension of the industrial-strength Java-based library Xelopes, and we illustrate its use by developing a multi-threaded Java program for the popular naive Bayes classification algorithm. In comparison with the popular MapReduce programming model, our resulting programs enable not only data-parallel, but also task-parallel implementation and a combination of both. Our experiments demonstrate an efficient parallelization and good scalability on multi-core processors.