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You searched for +publisher:"U of Denver" +contributor:("Ramakrishna Thurimella"). Showing records 1 – 2 of 2 total matches.

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1. Treinen, James J. Heuristics For Improved Enterprise Intrusion Detection.

Degree: PhD, Computer Science, 2009, U of Denver

One of the greatest challenges facing network operators today is the identification of malicious activity on their networks. The current approach is to deploy a set of intrusion detection sensors (IDSs) in various locations throughout the network and on strategic hosts. Unfortunately, the available intrusion detection technologies generate an overwhelming volume of false alarms, making the task of identifying genuine attacks nearly impossible. This problem is very difficult to solve even in networks of nominal size. The task of uncovering attacks in enterprise class networks quickly becomes unmanageable. Research on improving intrusion detection sensors is ongoing, but given the nature of the problem to be solved, progress is slow. Research simultaneously continues in the field of mining the set of alarms produced by IDS sensors. Varying techniques have been proposed to aggregate, correlate, and classify the alarms in ways that make the end result more concise and digestible for human analysis. To date, the majority of these techniques have been successful only in networks of modest size. As a means of extending this research to real world, enterprise scale networks, we propose 5 heuristics supporting a three-pronged approach to the systematic evaluation of large intrusion detection logs. Primarily, we provide a set of algorithms to assist operations personnel in the daunting task of ensuring that no true attack goes unnoticed. Secondly, we provide information that can be used to tune the sensors which are deployed on the network, reducing the overall alarm volume, thus mitigating the monitoring costs both in terms of hardware and labor, and improving overall accuracy. Third, we provide a means of discovering stages of attacks that were overlooked by the analyst, based on logs of known security incidents. Our techniques work by applying a combination of graph algorithms and Markovian stochastic processes to perform probabilistic analysis as to whether an alarm is a true or false positive. Using these techniques it is possible to significantly reduce the total number of alarms and hosts which must be examined manually, while simultaneously discovering attacks that had previously gone unnoticed. The proposed algorithms are also successful at the discovery of new profiles for multi-stage attacks, and can be used in the automatic generation of meta-alarms, or rules to assist the monitoring infrastructure in performing automated analysis. We demonstrate that it is possible to successfully rank hosts which comprise the vertices of an Alarm Graph in a manner such that those hosts which are of highest risk for being involved in attack are immediately highlighted for examination or inclusion on hot lists. We close with an evaluation of 3 sensor profiling algorithms, and show that the order in which alarms are generated is tightly coupled with whether or not they are false positives. We show that by using time based Markovian analysis of the alarms, we are able to identify alarms which have… Advisors/Committee Members: Ramakrishna Thurimella.

Subjects/Keywords: Anomaly Detection; Hidden Markov Model; Intrusion Detection; Markov Chain; Misuse Detection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Treinen, J. J. (2009). Heuristics For Improved Enterprise Intrusion Detection. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/657

Chicago Manual of Style (16th Edition):

Treinen, James J. “Heuristics For Improved Enterprise Intrusion Detection.” 2009. Doctoral Dissertation, U of Denver. Accessed December 08, 2019. https://digitalcommons.du.edu/etd/657.

MLA Handbook (7th Edition):

Treinen, James J. “Heuristics For Improved Enterprise Intrusion Detection.” 2009. Web. 08 Dec 2019.

Vancouver:

Treinen JJ. Heuristics For Improved Enterprise Intrusion Detection. [Internet] [Doctoral dissertation]. U of Denver; 2009. [cited 2019 Dec 08]. Available from: https://digitalcommons.du.edu/etd/657.

Council of Science Editors:

Treinen JJ. Heuristics For Improved Enterprise Intrusion Detection. [Doctoral Dissertation]. U of Denver; 2009. Available from: https://digitalcommons.du.edu/etd/657

2. Eltarjaman, Wisam Mohamed. Leveraging Client Processing for Location Privacy in Mobile Local Search.

Degree: PhD, Computer Science, 2016, U of Denver

Usage of mobile services is growing rapidly. Most Internet-based services targeted for PC based browsers now have mobile counterparts. These mobile counterparts often are enhanced when they use user's location as one of the inputs. Even some PC-based services such as point of interest Search, Mapping, Airline tickets, and software download mirrors now use user's location in order to enhance their services. Location-based services are exactly these, that take the user's location as an input and enhance the experience based on that. With increased use of these services comes the increased risk to location privacy. The location is considered an attribute that user's hold as important to their privacy. Compromise of one's location, in other words, loss of location privacy can have several detrimental effects on the user ranging from trivial annoyance to unreasonable persecution. More and more companies in the Internet economy rely exclusively on the huge data sets they collect about users. The more detailed and accurate the data a company has about its users, the more valuable the company is considered. No wonder that these companies are often the same companies that offer these services for free. This gives them an opportunity to collect more accurate location information. Research community in the location privacy protection area had to reciprocate by modeling an adversary that could be the service provider itself. To further drive this point, we show that a well-equipped service provider can infer user's location even if the location information is not directly available by using other information he collects about the user. There is no dearth of proposals of several protocols and algorithms that protect location privacy. A lot of these earlier proposals require a trusted third party to play as an intermediary between the service provider and the user. These protocols use anonymization and/or obfuscation techniques to protect user's identity and/or location. This requirement of trusted third parties comes with its own complications and risks and makes these proposals impractical in real life scenarios. Thus it is preferable that protocols do not require a trusted third party. We look at existing proposals in the area of private information retrieval. We present a brief survey of several proposals in the literature and implement two representative algorithms. We run experiments using different sizes of databases to ascertain their practicability and performance features. We show that private information retrieval based protocols still have long ways to go before they become practical enough for local search applications. We propose location privacy preserving mechanisms that take advantage of the processing power of modern mobile devices and provide configurable levels of location privacy. We propose these techniques both in the single query scenario and multiple query scenario. In single query scenario, the user issues a query to the server and obtains the answer. In the multiple query… Advisors/Committee Members: Ramakrishna Thurimella, Ph.D., Rinku Dewri, Ph.D..

Subjects/Keywords: Information Security; Location Privacy; Mobile Computing; Mobile Database; Private Information Retrieval; Computer Sciences; Databases and Information Systems; Information Security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Eltarjaman, W. M. (2016). Leveraging Client Processing for Location Privacy in Mobile Local Search. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1218

Chicago Manual of Style (16th Edition):

Eltarjaman, Wisam Mohamed. “Leveraging Client Processing for Location Privacy in Mobile Local Search.” 2016. Doctoral Dissertation, U of Denver. Accessed December 08, 2019. https://digitalcommons.du.edu/etd/1218.

MLA Handbook (7th Edition):

Eltarjaman, Wisam Mohamed. “Leveraging Client Processing for Location Privacy in Mobile Local Search.” 2016. Web. 08 Dec 2019.

Vancouver:

Eltarjaman WM. Leveraging Client Processing for Location Privacy in Mobile Local Search. [Internet] [Doctoral dissertation]. U of Denver; 2016. [cited 2019 Dec 08]. Available from: https://digitalcommons.du.edu/etd/1218.

Council of Science Editors:

Eltarjaman WM. Leveraging Client Processing for Location Privacy in Mobile Local Search. [Doctoral Dissertation]. U of Denver; 2016. Available from: https://digitalcommons.du.edu/etd/1218

.