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title={Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?},
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author={Einstein*†, A. and Podolsky*, B. and Rosen*, N.},
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abstract={In a complete theory there is an element corresponding to each element of reality. A sufficient condition for the reality of a physical quantity is the possibility of predicting it with certainty, without disturbing the system. In quantum mechanics in the case of two physical quantities described by non-commuting operators, the knowledge of one precludes the knowledge of the other. Then either (1) the description of reality given by the wave function in quantum mechanics is not complete or (2) these two quantities cannot have simultaneous reality. Consideration of the problem of making predictions concerning a system on the basis of measurements made on another system that had previously interacted with it leads to the result that if (1) is false then (2) is also false. One is thus led to conclude that the description of reality as given by a wave function is not complete.},
author = {Woralert, Chutitep and Liu, Chen and Blasingame, Zander},
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title = {Towards Effective Machine Learning Models for Ransomware Detection via Low-Level Hardware Information},
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year = {2024},
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isbn = {9798400712210},
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publisher = {Association for Computing Machinery},
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address = {New York, NY, USA},
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url = {https://doi.org/10.1145/3696843.3696847},
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doi = {10.1145/3696843.3696847},
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abstract = {In recent years, ransomware attacks have grown dramatically. New variants continually emerging make tracking and mitigating these threats increasingly difficult using traditional detection methods. As the landscape of ransomware evolves, there is a growing need for more advanced detection techniques. Neural networks have gained popularity as a method to enhance detection accuracy, by leveraging low-level hardware information such as hardware events as features for identifying ransomware attacks. In this paper, we investigated several state-of-the-art supervised learning models, including XGBoost, LightGBM, MLP, and CNN, which are specifically designed to handle time series data or image-based data for ransomware detection. We compared their detection accuracy, computational efficiency, and resource requirements for classification. Our findings indicate that particularly LightGBM, offer a strong balance of high detection accuracy, fast processing speed, and low memory usage, making them highly effective for ransomware detection tasks.},
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booktitle = {Proceedings of the International Workshop on Hardware and Architectural Support for Security and Privacy 2024},
title="{{\"U}ber einen die Erzeugung und Verwandlung des Lichtes betreffenden heuristischen Gesichtspunkt}",
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author={Albert Einstein},
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abstract={This is the abstract text.},
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journal={Ann. Phys.},
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volume={322},
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number={6},
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pages={132--148},
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year={1905},
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doi={10.1002/andp.19053220607},
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award={Albert Einstein receveid the **Nobel Prize in Physics** 1921 *for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect*},
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award_name={Nobel Prize}
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@phdthesis{woralert2024kernel,
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title={A Kernel Module Based Framework Towards Malware Detection Utilizing Low-level Hardware Information: A Dissertation},
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author={Woralert, Chutitep},
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year={2024},
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school={Department of Electrical and Computer Engineering Clarkson University}
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}
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@book{przibram1967letters,
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bibtex_show={true},
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title={Letters on wave mechanics},
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author={Einstein, Albert and Schrödinger, Erwin and Planck, Max and Lorentz, Hendrik Antoon and Przibram, Karl},
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year={1967},
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publisher={Vision},
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preview={wave-mechanics.gif},
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abbr={Vision}
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}
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@patent{chutitep_2024,
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abbr={US Patent},
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author = {Liu, Chen and Woralert, Chutitep},
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title = {Anomaly detection framework targeting ransomware using low-level hardware information},
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nationality = {United States},
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number = {538,875},
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day = {13},
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month = {June},
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year = {2024},
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html = {https://patents.google.com/patent/US20240193271A1/en}
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