F
Frederick T. Sheldon
Researcher at University of Idaho
Publications - 162
Citations - 1745
Frederick T. Sheldon is an academic researcher from University of Idaho. The author has contributed to research in topics: Smart grid & Formal specification. The author has an hindex of 20, co-authored 159 publications receiving 1451 citations. Previous affiliations of Frederick T. Sheldon include Oak Ridge National Laboratory & University of Texas at Arlington.
Papers
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Journal ArticleDOI
Blockchain: properties and misconceptions
Daniel Conte de Leon,Antonius Q. Stalick,Ananth A. Jillepalli,Michael Haney,Frederick T. Sheldon +4 more
TL;DR: The value and originality of this article is the disproving, through fact collection and systematic analysis, of current misconceptions about the properties of the blockchain and DLSs, and the discussion of challenges to achieving adequate trustworthiness along with the proposal of general avenues for possible solutions.
Journal ArticleDOI
Metrics for maintainability of class inheritance hierarchies
TL;DR: This paper investigates the work of Chidamber and Kemerer (1994) and Li (1998), and extends their work to apply specifically to the maintenance of a class inheritance hierarchy, and suggests new metrics for understandability and modifiability of aclass inheritance hierarchy.
Journal ArticleDOI
Quantifying security threats and their potential impacts: a case study
TL;DR: This paper illustrates a computational infrastructure that allows an analyst to estimate the security of a system in terms of the loss that each stakeholder stands to sustain as a result of security breakdowns by means of an e-commerce application.
Proceedings ArticleDOI
Detecting Stealthy False Data Injection Attacks in Power Grids Using Deep Learning
Mohammad Ashrafuzzaman,Yacine Chakhchoukh,Ananth A. Jillepalli,Predrag T. Tosic,Daniel Conte de Leon,Frederick T. Sheldon,Brian K. Johnson +6 more
TL;DR: This paper proposes a Deep Learning (DL) based method to accurately detect stealthy FDI attacks on the SE of power grid and compares the performance of the DL method with three popular machine learning algorithms, which are: gradient boosting machines (GBM), generalized linear modelings (GLM) and distributed random forests (DRF).
Proceedings ArticleDOI
A Methodology to Evaluate Agent Oriented Software Engineering Techniques
TL;DR: A baseline is developed herein to help us focus on the core of agent concepts throughout the comparative study and to investigate both the object-oriented and agent-oriented techniques that are available for constructing agent-based systems.