Effective Knowledge Transfer: A Terminological Perspective - Dismantling The Jargon Barrier To Knowledge About Computer Security

The research is concerned with the terminological problems that computer users experience when they try to formulate their knowledge needs and attempt to access information contained in computer manuals or online help systems while building up their knowledge. This is the recognized but unresolved problem of communication between the specialist and the layman. The initial hypothesis was that computer users, through their knowledge of language, have some prior knowledge of the subdomain of computing they are trying to come to terms with, and that language can be a facilitating mechanism, or an obstacle, in the development of that knowledge. Related to this is the supposition that users have a conceptual apparatus based on both theoretical knowledge and experience of the world, and of several domains of special reference related to the environment in which they operate. The theoretical argument was developed by exploring the relationship between knowledge and language, and considering the efficacy of terms as agents of special subject knowledge representation. Having charted in a systematic way the territory of knowledge sources and types, we were able to establish that there are many aspects of knowledge which cannot be represented by terms. This submission is important, as it leads to the realization that significant elements of knowledge are being disregarded in retrieval systems because they are normally expressed by language elements which do not enjoy the status of terms. Furthermore, we introduced the notion of `linguistic ease of retrieval' as a challenge to more conventional thinking which focuses on retrieval results

Effective knowledge transfer: a terminological perspective - Dismantling the jargon barrier to knowledge about computer security

The research is concerned with the terminological problems that computer users experience when they try to formulate their knowledge needs and attempt to access information contained in computer manuals or online help systems while building up their knowledge. This is the recognised but unresolved problem of communication between the specialist and the layman. The initial hypothesis was that computer users, through their knowledge of language, have some prior knowledge of the subdomain of computing they are trying to come to terms with, and that language can be a facilitating mechanism, or an obstacle, in the development of that knowledge. Related to this is the supposition that users have a conceptual apparatus based on both theoretical knowledge and experience of the world, and of several domains of special reference related to the environment in which they operate. The theoretical argument was developed by exploring the relationship between knowledge and language, and considering the efficacy of terms as agents of special subject knowledge representation. Having charted in a systematic way the territory of knowledge sources and types, we were able to establish that there are many aspects of knowledge which cannot be represented by terms. This submission is important, as it leads to the realisation that significant elements of knowledge are being disregarded in retrieval systems because they are normally expressed by language elements which do not enjoy the status of terms. Furthermore, we introduced the notion of `linguistic ease of retrieval' as a challenge to more conventional thinking which focuses on retrieval results

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Motivating Contributions for Home Computer Security.

Recently, malicious computer users have been compromising computers en masse and combining them to form coordinated botnets. The rise of botnets has brought the problem of home computers to the forefront of security. Home computer users commonly have insecure systems; these users do not have the knowledge, experience, and skills necessary to maintain a secure system. I take steps toward designing a socio-technical system that will hopefully help home computer users make better security decisions. Designing such a system requires additional knowledge before a successful system can be developed. First, more information is needed about the knowledge and skills that home computer users currently possess. I conducted an interview study of home computer users and identified eight distinct mental models of security threats; four are models of ``viruses,'' and four are models of ``hackers.'' The respondents in this study use the models to decide which security precautions should be used and which can be ignored. Second, to share information, users need an incentive to exert the time and effort required for sharing. I describe two mechanisms that can be used in social computing systems to encourage contribution. I illustrate the first mechanism, the side effect mechanism, by describing how it is used in a popular social bookmarking website. I also illustrate a design feature that is important when applying this mechanism: incentive alignment. The second mechanism that I describe is technically simple: set a minimum threshold and exclude users who don't contribute enough. I develop a theory of how users are likely to respond to such a mechanism and use that theory to characterize when such a mechanism should be used. Finally, I bring all of these findings together to suggest some preliminary design features for a socio-technical security system to help home computer users. While there are many unanswered questions, these design features can serve as a starting point for future work in the area.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64653/1/rwash_1.pd

Undergraduate Bulletin, 2016-2017

https://red.mnstate.edu/bulletins/1100/thumbnail.jp

The Bulletin, Undergraduate Catalog 2015-2016 (2015)

https://red.mnstate.edu/bulletins/1098/thumbnail.jp

The Bulletin, Undergraduate Catalog 2013-2014 (2013)

https://red.mnstate.edu/bulletins/1096/thumbnail.jp

The Bulletin, Undergraduate Catalog 2014-2015 (2014)

https://red.mnstate.edu/bulletins/1097/thumbnail.jp