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Full-Text Articles in Physical Sciences and Mathematics
Steganalysis Embedding Percentage Determination With Learning Vector Quantization, Benjamin M. Rodriguez, Gilbert L. Peterson, Kenneth W. Bauer, Sos S. Agaian
Steganalysis Embedding Percentage Determination With Learning Vector Quantization, Benjamin M. Rodriguez, Gilbert L. Peterson, Kenneth W. Bauer, Sos S. Agaian
Faculty Publications
Steganography (stego) is used primarily when the very existence of a communication signal is to be kept covert. Detecting the presence of stego is a very difficult problem which is made even more difficult when the embedding technique is not known. This article presents an investigation of the process and necessary considerations inherent in the development of a new method applied for the detection of hidden data within digital images. We demonstrate the effectiveness of learning vector quantization (LVQ) as a clustering technique which assists in discerning clean or non-stego images from anomalous or stego images. This comparison is conducted …
Fuzzy State Aggregation And Policy Hill Climbing For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Fuzzy State Aggregation And Policy Hill Climbing For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Faculty Publications
Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the operating environment changes. Additionally, by applying reinforcement learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the fastest policy hill …
A Multidiscipline Approach To Mitigating The Insider Threat, Jonathan W. Butts, Robert F. Mills, Gilbert L. Peterson
A Multidiscipline Approach To Mitigating The Insider Threat, Jonathan W. Butts, Robert F. Mills, Gilbert L. Peterson
Faculty Publications
Preventing and detecting the malicious insider is an inherently difficult problem that expands across many areas of expertise such as social, behavioral and technical disciplines. Unfortunately, current methodologies to combat the insider threat have had limited success primarily because techniques have focused on these areas in isolation. The technology community is searching for technical solutions such as anomaly detection systems, data mining and honeypots. The law enforcement and counterintelligence communities, however, have tended to focus on human behavioral characteristics to identify suspicious activities. These independent methods have limited effectiveness because of the unique dynamics associated with the insider threat. The …
Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez
Multiple Masks-Based Pixel Comparison Steganalysis Method For Mobile Imaging, Sos S. Agaian, Gilbert L. Peterson, Benjamin M. Rodriguez
Faculty Publications
No abstract provided.
Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Fuzzy State Aggregation And Off-Policy Reinforcement Learning For Stochastic Environments, Dean C. Wardell, Gilbert L. Peterson
Faculty Publications
Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually learn even as the environment it is operating in changes. This ability to learn in an unsupervised manner in a changing environment is applicable in complex domains through the use of function approximation of the domain’s policy. The function approximation presented here is that of fuzzy state aggregation. This article presents the use of fuzzy state aggregation with the current policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF), exceeding the learning rate …