Computer Engineering Commons^™

An Fpga Based Implementation Of The Exact Stochastic Simulation Algorithm, Phani Bharadwaj Vanguri

Masters Theses

Mathematical and statistical modeling of biological systems is a desired goal for many years. Many biochemical models are often evaluated using a deterministic approach, which uses differential equations to describe the chemical interactions. However, such an approach is inaccurate for small species populations as it neglects the discrete representation of population values, presents the possibility of negative populations, and does not represent the stochastic nature of biochemical systems. The Stochastic Simulation Algorithm (SSA) developed by Gillespie is able to properly account for these inherent noise fluctuations. Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations …

Adaptive Performance And Power Management In Distributed Computing Systems, Ming Chen

Doctoral Dissertations

The complexity of distributed computing systems has raised two unprecedented challenges for system management. First, various customers need to be assured by meeting their required service-level agreements such as response time and throughput. Second, system power consumption must be controlled in order to avoid system failures caused by power capacity overload or system overheating due to increasingly high server density. However, most existing work, unfortunately, either relies on open-loop estimations based on off-line profiled system models, or evolves in a more ad hoc fashion, which requires exhaustive iterations of tuning and testing, or oversimplifies the problem by ignoring the coupling …

Cyber Profiling For Insider Threat Detection, Akaninyene Walter Udoeyop

Masters Theses

Cyber attacks against companies and organizations can result in high impact losses that include damaged credibility, exposed vulnerability, and financial losses. Until the 21st century, insiders were often overlooked as suspects for these attacks. The 2010 CERT Cyber Security Watch Survey attributes 26 percent of cyber crimes to insiders. Numerous real insider attack scenarios suggest that during, or directly before the attack, the insider begins to behave abnormally. We introduce a method to detect abnormal behavior by profiling users. We utilize the k-means and kernel density estimation algorithms to learn a user’s normal behavior and establish normal user profiles based …

A Novel Technique For Ctis Image-Reconstruction, Mitchel Dewayne Horton

Doctoral Dissertations

Computed tomography imaging spectrometer (CTIS) technology is introduced and its use is discussed. An iterative method is presented for CTIS image-reconstruction in the presence of both photon noise in the image and post-detection Gaussian system noise. The new algorithm assumes the transfer matrix of the system has a particular structure. Error analysis, performance evaluation, and parallelization of the algorithm is done. Complexity analysis is performed for the proof of concept code developed. Future work is discussed relating to potential improvements to the algorithm.

An intuitive explanation for the success of the new algorithm is that it reformulates the image reconstruction …

Anomaly Detection In Unknown Environments Using Wireless Sensor Networks, Yuanyuan Li

Doctoral Dissertations

This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected …

Gpu Implementation Of A Novel Approach To Cramer’S Algorithm For Solving Large Scale Linear Systems, Rosanne Lane West

Masters Theses

Scientific computing often requires solving systems of linear equations. Most software pack- ages for solving large-scale linear systems use Gaussian elimination methods such as LU- decomposition. An alternative method, recently introduced by K. Habgood and I. Arel, involves an application of Cramer’s Rule and Chio’s condensation to achieve a better per- forming system for solving linear systems on parallel computing platforms. This thesis describes an implementation of this algorithm on an nVidia graphics processor card us- ing the CUDA language. Increased performance, relative to the serial implementation, is demonstrated, paving the way for future parallel realizations of the scheme.

Performance Evaluation Of Memory And Computationally Bound Chemistry Applications On Streaming Gpgpus And Multi-Core X86 Cpus, Frederick E. Weber Iii

Masters Theses

In recent years, multi-core processors have come to dominate the field in desktop and high performance computing. Graphics processors traditionally used in CAD, video games, and other 3-d applications, have become more programmable and are now suitable for general purpose computing. This thesis explores multi-core processors and GPU performance and limitations in two computational chemistry applications: a memory bound component of ab-initio modeling and a computationally bound Monte Carlo simulation. For the applications presented in this thesis, exploiting multiple processors is done using a variety of tools and languages including OpenMP and MKL. Brook+ and the Compute Abstraction Layer streaming …

Dynamic Application Level Security Sensors, Christopher Thomas Rathgeb

Masters Theses

The battle for cyber supremacy is a cat and mouse game: evolving threats from internal and external sources make it difficult to protect critical systems. With the diverse and high risk nature of these threats, there is a need for robust techniques that can quickly adapt and address this evolution. Existing tools such as Splunk, Snort, and Bro help IT administrators defend their networks by actively parsing through network traffic or system log data. These tools have been thoroughly developed and have proven to be a formidable defense against many cyberattacks. However, they are vulnerable to zero-day attacks, slow attacks, …