1,474 research outputs found

    Quasi two-level PWM operation of a nine-arm modular multilevel converter for six-phase medium-voltage motor drives

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    This paper proposes a hybrid converter for medium-voltage six-phase machine drive systems that mixes the operation of a traditional two-level voltage-source inverter and the modular multilevel converter (MMC) to enable operation over a wide frequency range. Topologically, the proposed converter consists of nine arms resembling two sets of three-phase MMCs with three common arms, yielding a nine-arm MMC with a 25% reduction in the number of employed arms compared to a traditional dual three-phase MMC. The multilevel property of a standard MMC is emulated in the proposed converter, however on a two-level basis, resulting in a stepped two-level output voltage waveform. The proposed converter has a reduced footprint with advantages of small voltage steps, modular structure, and ease of scalability. Further, it is able to drive high-power six-phase machines within low operating frequencies at the rated torque. The operating principle of the converter is elaborated, and its modulation scheme is discussed. The features of the proposed converter are verified through simulations and experimentally

    Hidden Markov models and alert correlations for the prediction of advanced persistent threats

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    YesCyber security has become a matter of a global interest, and several attacks target industrial companies and governmental organizations. The advanced persistent threats (APTs) have emerged as a new and complex version of multi-stage attacks (MSAs), targeting selected companies and organizations. Current APT detection systems focus on raising the detection alerts rather than predicting APTs. Forecasting the APT stages not only reveals the APT life cycle in its early stages but also helps to understand the attacker's strategies and aims. This paper proposes a novel intrusion detection system for APT detection and prediction. This system undergoes two main phases; the first one achieves the attack scenario reconstruction. This phase has a correlation framework to link the elementary alerts that belong to the same APT campaign. The correlation is based on matching the attributes of the elementary alerts that are generated over a configurable time window. The second phase of the proposed system is the attack decoding. This phase utilizes the hidden Markov model (HMM) to determine the most likely sequence of APT stages for a given sequence of correlated alerts. Moreover, a prediction algorithm is developed to predict the next step of the APT campaign after computing the probability of each APT stage to be the next step of the attacker. The proposed approach estimates the sequence of APT stages with a prediction accuracy of at least 91.80%. In addition, it predicts the next step of the APT campaign with an accuracy of 66.50%, 92.70%, and 100% based on two, three, and four correlated alerts, respectively.The Gulf Science, Innovation and Knowledge Economy Programme of the U.K. Government under UK-Gulf Institutional Link Grant IL 279339985 and in part by the Engineering and Physical Sciences Research Council (EPSRC), U.K., under Grant EP/R006385/1
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