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However, side-channel information often comes in the form of unlabeled time series of a proxy variable of the activity.
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However, side-channel information often comes in the form of unlabeled time series of a proxy variable of the activity.
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Enabling the definition and enforcement of high-level security policies requires extracting the state or activity of the system from the input data.
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Enabling the definition and enforcement of high-level security policies requires extracting the state or activity of the system from the input data.
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We present in this paper a novel time series, one-shot pattern locator and classifier called Machine Activity Detector (MAD) specifically designed and evaluated for side-channel analysis.
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We present in this paper a novel time series, one-shot pattern locator and classifier called Machine Activity Detector (MAD) specifically designed and evaluated for side-channel analysis.
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We evaluate MAD in two case studies on a variety of machines and datasets where it outperforms other traditional state detection solutions and present formidable performances for security rules enforcement.
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We evaluate MAD in two case studies on a variety of machines and datasets where it outperforms other traditional state detection solutions and presents formidable performances for security rules enforcement.
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Results of state detection with MAD enable the definition and verification of high-level security rules to detect various attacks without any interaction with the monitored machine.
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Results of state detection with MAD enable the definition and verification of high-level security rules to detect various attacks without any interaction with the monitored machine.
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\end{abstract}
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\end{abstract}
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%\IEEEoverridecommandlockouts
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%\IEEEoverridecommandlockouts
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%\vspace{1.5ex}
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%\vspace{1.5ex}
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\begin{keywords}
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\begin{keywords}
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\itshape component; formatting; style; styling; insert (key words)
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\itshape Side-Channel Analysis; Intrusion Detection.
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\end{keywords}
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\end{keywords}
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% no keywords
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% no keywords
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