Computer Engineering Commons^™

Scare Tactics, Tiago Martines, Gabriel Ortega, Karan Sahu, Lucas Pereira Vasconcelos, Henrique Silva Chaltein De Almeida

Theses

It is the purpose of this document to describe the design and development processes of Scare Tactics. The game will be discussed in further detail as it relates to several areas, such as market analysis, development process, game design, technical design, and each team members’ individual area of background research. The research areas include asymmetrical game design, level design, game engine architecture, real-time graphics, user interface design, networking and artificial intelligence.

As part of the team’s market analysis, other games featuring asymmetric gameplay are discussed. The games described in this section serve as inspirations for asymmetric game design. Some …

Reliability-Performance Trade-Offs In Photonic Noc Architectures, Pradheep Khanna Kaliraj

Theses

Advancements in the field of chip fabrication has facilitated in integrating more number of transistors in a given area which lead to the era of multi-core processors. Interconnect became the bottleneck for the multi-core processors as the number of cores in a chip increased. The traditional bus based architectures, which are currently used in the processors, cannot scale up to support the increasing number of cores in a multi-core chip. Hence, Network-on-Chip (NoC) is the preferred communication backbone for modern multicore chips. However, the multi-hop data transmission using wireline interconnects result in high energy dissipation and latency. Hence, many alternative …

Segmentation And Model Generation For Large-Scale Cyber Attacks, Steven E. Strapp

Theses

Raw Cyber attack traffic can present more questions than answers to security analysts. Especially with large-scale observables it is difficult to identify which packets are relevant and what attack behaviors are present. Many existing works in Host or Flow Clustering attempt to group similar behaviors to expedite analysis; these works often phrase the problem directly as offline unsupervised machine learning. This work proposes online processing to simultaneously model coordinating actors and segment traffic that is relevant to a target of interest, all while it is being received. The goal is not just to aggregate similar attack behaviors, but to provide …