
@article {JohRog15A,
	title = {An FPGA Architecture for the Recovery of WPA/WPA2 Keys},
	journal = {Journal of Circuits, Systems, and Computers (JCSC)},
	volume = {24},
	year = {2015},
	abstract = {Wi-Fi protected access (WPA) has provided serious improvements over the now deprecated wired equivalent privacy (WEP) protocol. WPA, however, still has some flaws that allow an attacker to obtain the passphrase. One of these flaws is exposed when the access point (AP) is operating in the WPA personal mode. This is the most common mode, as it is the  quickest and easiest to configure. This vulnerability requires the attacker to capture the traffic from the four-way handshake between the AP and client, and then have enough compute time to reverse the passphrase. Increasing the rate at which passphrases can be reversed reduces the amount of time required to construct a repository of service set identifiers (SSIDs) and passphrases, which can increase the chances an attack is successful, or, alternatively, reduce the difficulty of auditing a wireless network for security purposes. This work focuses on creating a Field programmable gate array (FPGA)-based architecture to accelerate the generation of a WPA/WPA2 pairwise master key (PMK) lookup table (LUT) for the recovery of  the passphrase, with special emphasis on the secure hash algorithm-1 (SHA-1) implementation. PMK generation relies heavily on SHA-1 hashing and, as this work shows, an optimized SHA-1 implementation can achieve up to a 40  speedup over an unoptimized implementation when generating PMKs},
	author = {Tyler Johnson and Daniel Roggow and Phillip Jones and Joseph Zambreno}
}
@conference {TowSun15A,
	title = {k-NN Text Classification using an FPGA-Based Sparse Matrix Vector Multiplication Accelerator},
	booktitle = {Proceedings of the IEEE International Conference on Electro/Information Technology (EIT)},
	year = {2015},
	month = {May},
	abstract = {Text classification is an important enabling technology for a wide range of applications such as Internet search, email filtering, network intrusion detection, and data mining electronic documents in general. The k Nearest Neighbors (k-NN) text classification algorithm is among the most accurate classification approaches, but is also among the most computationally expensive. In this paper, we propose accelerating k-NN using a novel reconfigurable hardware based architecture. More specifically, we accelerate a k-NN application{\textquoteright}s core with an FPGA-based sparse matrix vector multiplication coprocessor. On average our implementation shows a speed up factor of 15 over a naive single threaded CPU implementation of k-NN text classification for our datasets, and a speed up factor of 1.5 over a 32-threaded parallelized CPU implementation.},
	author = {Kevin Townsend and Song Sun and Tyler Johnson and Osama Attia and Phillip Jones and Joseph Zambreno}
}
@conference {AttJoh14A,
	title = {CyGraph: A Reconfigurable Architecture for Parallel Breadth-First Search},
	booktitle = {Proceedings of the Reconfigurable Architectures Workshop (RAW)},
	year = {2014},
	month = {May},
	abstract = {Large-scale graph structures are considered a keystone for many emerging high-performance computing applications in which Breadth-First Search (BFS) is an important building block. For such graph structures, BFS operations tends to be memory-bound rather than compute-bound. In this paper, we present an efficient reconfigurable architecture for parallel BFS that adopts new optimizations for utilizing memory bandwidth. Our architecture utilizes a custom graph representation based on compressed-sparse raw format (CSR), as well as a restructuring of the conventional BFS algorithm. By taking maximum advantage of available memory bandwidth, our architecture continuously keeps our processing elements active. Using a commercial high-performance reconfigurable computing system (the Convey HC-2), our results demonstrate a 5x speedup over previously published FPGA-based implementations.
},
	author = {Osama Attia and Tyler Johnson and Kevin Townsend and Phillip Jones and Joseph Zambreno}
}
