Experimental design methodologies for the identification of Michaelis-Menten type kinetics

The main objective of this work was to investigate the application of experimental design techniques for the identification of Michaelis-Menten kinetic parameters. More specifically, this study attempts to elucidate the relative advantages/disadvantages of employing complex experimental design techniques in relation to equidistant sampling when applied to different reactor operation modes. All studies were supported by simulation data of a generic enzymatic process that obeys to the Michaelis-Menten kinetic equation. Different aspects were investigated, such as the influence of the reactor operation mode (batch, fed-batch with pulse wise feeding and fed-batch with continuous feeding) and the experimental design optimality criteria on the effectiveness of kinetic parameters identification. The following experimental design optimality criteria were investigated: 1) minimization of the sum of the diagonal of the Fisher information matrix (FIM) inverse (A-criterion), 2) maximization of the determinant of the FIM (D-criterion), 3) maximization of the smallest eigenvalue of the FIM (E-criterion) and 4) minimization of the quotient between the largest and the smallest eigenvalue (modified E-criterion). The comparison and assessment of the different methodologies was made on the basis of the Cramér-Rao lower bounds (CRLB) error in respect to the parameters vmax and Km of the Michaelis-Menten kinetic equation. In what concerns the reactor operation mode, it was concluded that fed-batch (pulses) is better than batch operation for parameter identification. When the former operation mode is adopted, the vmax CRLB error is lowered by 18.6 % while the Km CRLB error is lowered by 26.4 % when compared to the batch operation mode. Regarding the optimality criteria, the best method was the A-criterion, with an average vmax CRLB of 6.34 % and 5.27 %, for batch and fed-batch (pulses), respectively, while presenting a Km’s CRLB of 25.1 % and 18.1 %, for batch and fed-batch (pulses), respectively. As a general conclusion of the present study, it can be stated that experimental design is justified if the starting parameters CRLB errors are inferior to 19.5 % (vmax) and 45% (Km), for batch processes, and inferior to 42 % and to 50% for fed-batch (pulses) process. Otherwise equidistant sampling is a more rational decision. This conclusion clearly supports that, for fed-batch operation, the use of experimental design is likely to largely improve the identification of Michaelis-Menten kinetic parameters.

Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.