Decision Support for Estimation of the Utility of Software and E-mail
|Title:||Decision Support for Estimation of the Utility of Software and E-mail|
|Series:||Blekinge Institute of Technology Licentiate Dissertion Series|
|Publisher:||Blekinge Institute of Technology|
|Organization:||Blekinge Institute of Technology|
|Department:||School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
|Abstract:||Background: Computer users often need to distinguish between good and bad instances of software and e-mail messages without the aid of experts. This decision process is further complicated as the perception of spam and spyware varies between individuals. As a consequence, users can benefit from using a decision support system to make informed decisions concerning whether an instance is good or bad.
Objective: This thesis investigates approaches for estimating the utility of e-mail and software. These approaches can be used in a personalized decision support system. The research investigates the performance and accuracy of the approaches.
Method: The scope of the research is limited to the legal grey- zone of software and e-mail messages. Experimental data have been collected from academia and industry. The research methods used in this thesis are simulation and experimentation. The processing of user input, along with malicious user input, in a reputation system for software were investigated using simulations. The preprocessing optimization of end user license agreement classification was investigated using experimentation. The impact of social interaction data in regards to personalized e-mail classification was also investigated using experimentation.
Results: Three approaches were investigated that could be adapted for a decision support system. The results of the investigated reputation system suggested that the system is capable, on average, of producing a
rating ±1 from an objects correct rating. The results of the preprocessing optimization of end user license agreement classification suggested negligible impact. The results of using social interaction information in e-mail classification suggested that accurate spam detectors can be generated from the low-dimensional social data model alone, however, spam detectors generated from combinations of the traditional and social models were more accurate.
Conclusions: The results of the presented approaches suggestthat it is possible to provide decision support for detecting software that might be of low utility to users. The labeling of instances of software and e-mail messages that are in a legal grey-zone can assist users in avoiding an instance of low utility, e.g. spam and spyware. A limitation in the approaches is that isolated implementations will yield unsatisfactory results in a real world setting. A combination of the
approaches, e.g. to determine the utility of software, could yield improved results.