7,453 research outputs found
The Best Trail Algorithm for Assisted Navigation of Web Sites
We present an algorithm called the Best Trail Algorithm, which helps solve
the hypertext navigation problem by automating the construction of memex-like
trails through the corpus. The algorithm performs a probabilistic best-first
expansion of a set of navigation trees to find relevant and compact trails. We
describe the implementation of the algorithm, scoring methods for trails,
filtering algorithms and a new metric called \emph{potential gain} which
measures the potential of a page for future navigation opportunities.Comment: 11 pages, 11 figure
How to Compare the Scientific Contributions between Research Groups
We present a method to analyse the scientific contributions between research
groups. Given multiple research groups, we construct their journal/proceeding
graphs and then compute the similarity/gap between them using network analysis.
This analysis can be used for measuring similarity/gap of the topics/qualities
between research groups' scientific contributions. We demonstrate the
practicality of our method by comparing the scientific contributions by Korean
researchers with those by the global researchers for information security in
2006 - 2008. The empirical analysis shows that the current security research in
South Korea has been isolated from the global research trend
A Nine Month Report on Progress Towards a Framework for Evaluating Advanced Search Interfaces considering Information Retrieval and Human Computer Interaction
This is a nine month progress report detailing my research into supporting users in their search for information, where the questions, results or even thei
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
CPAS/CCM experiences: Perspectives for AI/ES research in accounting
https://egrove.olemiss.edu/dl_proceedings/1111/thumbnail.jp
SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources
SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance
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