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1. A Model for Automating the Abstraction of Planning Problems in a Narrative Context

   https://doi.org/10.1609/aiide.v20i1.31864  

   Fisher, Mira; Ware, Stephen (November 2024, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Contemporary automated planning research emphasizes the use of domain knowledge abstractions like heuristics to improve search efficiency. Transformative automated abstraction techniques which decompose or otherwise reformulate the problem have a limited presence, owing to poor performance in key metrics like plan length and time efficiency. In this paper, we argue for a reexamination of these transformative techniques in the context of narrative planning, where classical metrics are less appropriate. We propose a model for automating abstraction by decomposing a planning problem into subproblems which serve as abstract features of the problem. We demonstrate the application of this approach on a low-level problem and discuss key features of the resulting abstract problem. Plans in the abstract problem are shorter, representing summaries of low-level plans, but can be directly translated into low-level plans for the original problem. 

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2. Anomaly Detection for Science DMZs Using System Performance Data

   https://doi.org/10.1109/ICNC47757.2020.9049695  

   Gegan, Ross; Mao, Christina; Ghosal, Dipak; Bishop, Matt; Peisert, Sean (February 2020, 2020 International Conference on Computing, Networking and Communications)

   null (Ed.)

   Science DMZs are specialized networks that enable large-scale distributed scientific research, providing efficient and guaranteed performance while transferring large amounts of data at high rates. The high-speed performance of a Science DMZ is made viable via data transfer nodes (DTNs), therefore they are a critical point of failure. DTNs are usually monitored with network intrusion detection systems (NIDS). However, NIDS do not consider system performance data, such as network I/O interrupts and context switches, which can also be useful in revealing anomalous system performance potentially arising due to external network based attacks or insider attacks. In this paper, we demonstrate how system performance metrics can be applied towards securing a DTN in a Science DMZ network. Specifically, we evaluate the effectiveness of system performance data in detecting TCP-SYN flood attacks on a DTN using DBSCAN (a density-based clustering algorithm) for anomaly detection. Our results demonstrate that system interrupts and context switches can be used to successfully detect TCP-SYN floods, suggesting that system performance data could be effective in detecting a variety of attacks not easily detected through network monitoring alone. 

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3. Travel Kinematics in Virtual Reality Increases Learning Efficiency

   https://doi.org/10.1109/VL/HCC51201.2021.9576317  

   Nersesian, Eric and (October 2021, IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC))

   Virtual reality (VR) computer interfaces show promise for improving societal communication and representation of information due to their unique ability to be placed spatially around the user in three-dimensional (3D) space. This opens new possibilities for presentation and user interaction with the target information, and may be especially impactful for the education of science, technology, engineering, and mathematics (STEM) professionals. Simulations and visualizations have been shown in research studies to improve the efficiency of STEM learners compared to the less sensorimotor rich learning mediums of live instruction and textbook reading. Yet, learning science research into immersive computer simulation environments for educational applications remains limited. To address this research gap, we analyzed a fundamental VR interface capability, virtual environmental traversal, and its impact on participants' learning. We altered the traversal ability between two groups of STEM learners within the same virtual environment and compared their performance. Findings point that VR computer interfaces, regardless of environmental traversal, are suitable STEM learning environments, but that environmental traversal can increase learning efficiency. 

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4. Insider Attack Detection for Science DMZs Using System Performance Data

   https://doi.org/10.1109/CNS48642.2020.9162260  

   Gegan, Ross; Perry, Brian; Ghosal, Dipak; Bishop, Matt (June 2020, 2020 IEEE Conference on Communications and Network Security (CNS))

   null (Ed.)

   The science DMZ is a specialized network model developed to guarantee secure and efficient transfer of data for large-scale distributed research. To enable a high level of performance, the Science DMZ includes dedicated data transfer nodes (DTNs). Protecting these DTNs is crucial to maintaining the overall security of the network and the data, and insider attacks are a major threat. Although some limited network intrusion detection systems (NIDS) are deployed to monitor DTNs, this alone is not sufficient to detect insider threats. Monitoring for abnormal system behavior, such as unusual sequences of system calls, is one way to detect insider threats. However, the relatively predictable behavior of the DTN suggests that we can also detect unusual activity through monitoring system performance, such as CPU and disk usage, along with network activity. In this paper, we introduce a potential insider attack scenario, and show how readily available system performance metrics can be employed to detect data tampering within DTNs, using DBSCAN clustering to actively monitor for unexpected behavior. 

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5. Robust Performance Metrics for Authentication Systems

   https://doi.org/https://dx.doi.org/10.14722/ndss.2019.23351  

   Sugrim, Shridatt; Liu, Can; McLean, Meghan; Lindqvist, Janne (February 2019, Network and Distributed Systems Security (NDSS) Symposium 2019)

   Research has produced many types of authentication systems that use machine learning. However, there is no consistent approach for reporting performance metrics and the reported metrics are inadequate. In this work, we show that several of the common metrics used for reporting performance, such as maximum accuracy (ACC), equal error rate (EER) and area under the ROC curve (AUROC), are inherently flawed. These common metrics hide the details of the inherent trade-offs a system must make when implemented. Our findings show that current metrics give no insight into how system performance degrades outside the ideal conditions in which they were designed. We argue that adequate performance reporting must be provided to enable meaningful evaluation and that current, commonly used approaches fail in this regard. We present the unnormalized frequency count of scores (FCS) to demonstrate the mathematical underpinnings that lead to these failures and show how they can be avoided. The FCS can be used to augment the performance reporting to enable comparison across systems in a visual way. When reported with the Receiver Operating Characteristics curve (ROC), these two metrics provide a solution to the limitations of currently reported metrics. Finally, we show how to use the FCS and ROC metrics to evaluate and compare different authentication systems. 

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