Browsing by Subject "Analysis techniques"

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    Empirical mode decomposition aided by adaptive low pass filtering
    (IEEE, 2012) Öztürk, Onur; Arıkan, Orhan; Çetin, A. Enis
    Empirical Mode Decomposition (EMD) is an adaptive signal analysis technique which derives its basis functions from the signal itself. EMD is realized through successive iterations of a sifting process requiring local mean computation. For that purpose, local minima and maxima of the signal are assumed to constitute proper local time scales. EMD lacks accuracy, however, experiencing the so-called mode mixing phenomenon in the presence of noise which creates artificial extrema. In this paper, we propose adaptively filtering the signal in Discrete Cosine Transform domain before each local mean computation step to prevent mode mixing. Denoising filter thresholds are optimized for a product form criterion which is a function of the preserved energy and the eliminated number of extrema of the signal after filtering. Results obtained from synthetic signals reveal the potential of the proposed technique. © 2012 IEEE.
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    Preventing unauthorized data flows
    (Springer, Cham, 2017) Uzun, Emre; Parlato, G.; Atluri, V.; Ferrara, A. L.; Vaidya, J.; Sural, S.; Lorenzi, D.
    Trojan Horse attacks can lead to unauthorized data flows and can cause either a confidentiality violation or an integrity violation. Existing solutions to address this problem employ analysis techniques that keep track of all subject accesses to objects, and hence can be expensive. In this paper we show that for an unauthorized flow to exist in an access control matrix, a flow of length one must exist. Thus, to eliminate unauthorized flows, it is sufficient to remove all one-step flows, thereby avoiding the need for expensive transitive closure computations. This new insight allows us to develop an efficient methodology to identify and prevent all unauthorized flows leading to confidentiality and integrity violations. We develop separate solutions for two different environments that occur in real life, and experimentally validate the efficiency and restrictiveness of the proposed approaches using real data sets. © IFIP International Federation for Information Processing 2017.