Thesis (M.S.)--West Virginia University, 2003 / Title from document title page. Document formatted into pages; contains vi, 87 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 86-87).

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains vii, 104 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 95-104).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains vii, 97 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 92-97).

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains ix, 80 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 76-80).

Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501.

<p>Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501.</p>

"Each computer on a network has an OS Fingerprint that can be collected through various applications. Because of the complexity of network systems, vulnerabilities and exploitations of the same to gain access to systems will always be a problem. Those wishing to attack a system can use the OS Fingerprint to identify the types of vulnerabilities and software exploits that will be effective against the system. This paper discusses how system vulnerabilities become exploited and used by network attackers. Because OS Fingerprints are one of many tools network attackers will use to identify and attack a system, concealing a system's OS Fingerprint becomes an important part of securing that system. To demonstrate the capability of concealing the OS Fingerprint of a system, a prototype system was developed. This prototype changed the OS Fingerprint of a Linux system so that it matched a Windows NT system.

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This thesis represents the results of a study into using fingerprints generated according to the Rabin-Karp Algorithm, and a database LevelDB to achieve Text Search times below GREP, which is a standard command-line UNIX text search tool. Text Search is a set of algorithms that find a string of characters called a Search Pattern in a much larger string of characters in a document we call a text file. The Rabin-Karp Algorithm iterates through a text file converting character strings into fingerprints at each location. A fingerprint numerically represents a window length string of characters to the left of its location. The algorithm compares the calculated fingerprint to the Search Pattern’s fingerprint. When fingerprints are not equal, we can guarantee the corresponding strings will not match. Whereas when fingerprints are, the strings probably match. A verification process confirms matches by checking respective characters. Our application emerges after making the following major changes to the Rabin-Karp Algorithm. First, we employ a two-step technique rather than one. During step 1, the preprocessing step, we calculate and store fingerprints in a LevelDB database called an Index Database. This is our first major change unique to us. Step 2, the matching step, is our second unique change. We use the Index Database to look-up the Search Pattern’s fingerprint and gather its set of locations. Finally, we allow the pattern to be any length relative to the window length. We even created an equation to check if the difference in length is too long for the fingerprint’s number system base. We facilitated our performance experiments by first building our application and testing it against GREP for a wide range of different parameters. Our conclusions and recommendations determine that although we currently only outperform GREP in about half the cases, we identify some promising opportunities to modify some parts of our application so that we can outperform GREP in all instances. / UOIT

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