Książki, monografie, podręczniki, rozdziały (WFTIiMS)

Stały URI dla kolekcjihttp://hdl.handle.net/11652/173

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Autorsearch.filters.applied.operator.equals: search.filters.author.Morawski, MichałData rozpoczęcia: 2011

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Teraz wyświetlane 1 - 4 z 4
  • Pozycja
    MPTCP Congestion Control Algorithms for Streaming Applications – Performance Evaluation in Public Networks
    (Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Łukasz Piotr; Ignaciuk, Przemysław; Morawski, Michał
    Efficient data transfer for high-quality streaming requires fast speed, low latency, and stable transmission parameters. Utilizing multiple communication paths is a promising solution for improving performance. This paper evaluates the most common MPTCP congestion control algorithms in the context of streaming applications in the open Internet. The results show that the BALIA algorithm is the most effective CC algorithm for multi-path streaming. This algorithm achieves the lowest path delay and Head-of-Line blocking degree with consistent throughput. Conversely, the MPTCP CC algorithm wVegas exhibits the weakest performance.
  • Pozycja
    Integrating Anomaly Detection for Enhanced Data Protection in Cloud-Based Applications
    (Wydawnictwo Politechniki Łódzkiej, 2023) Czerkas, Konrad; Drozd, Michał; Duraj, Agnieszka; Lichy, Krzysztof; Lipiński, Piotr; Morawski, Michał; Napieralski, Piotr; Puchała, Dariusz; Kwapisz, Marcin; Warcholiński, Adrian; Karbowańczyk, Michał; Wosiak, Piotr
    In this research, anomaly detection techniques and artificial neural networks were employed to address the issue of attacks on cluster computing systems. The study investigated the detection of Distributed Denial of Service (DDoS) and Partition attacks by monitoring metrics such as network latency, data transfer rate, and number of connections. Additionally, outlier detection algorithms, namely Local Outlier Factor (LOF) and COF, as well as ARIMA and SHESD models were tested for anomaly detection. Two types of neural network architectures, multi-layer perceptron (MLP) and recursive LSTM networks, were used to detect attacks by classifying events as “attack” or “no attack”. The study underscores the importance of implementing proactive security measures to protect cluster computing systems from cyber threats.
  • Pozycja
    Energy Dissipation Anomalies in Buildings
    (Wydawnictwo Politechniki Łódzkiej, 2023) Morawski, Michał; Tomczyk, Arkadiusz; Idaczyk, Maciej
  • Pozycja
    AI-driven Ecodriving and ETA Solutions for Truck Transport
    (Wydawnictwo Politechniki Łódzkiej, 2023) Lipiński, Piotr; Morawski, Michał; Napieralski, Piotr; Nowok, Paweł; Zawiślak, Bartosz; Hojdys, Leszek; Lazar, Marcin; Lazarek, Przemysław; Zając, Norbert; Pizoń, Sylwester; Jakubiec, Rafał; Sienkiewicz, Jacek; Gołąbek, Sebastian; Kabocik, Mateusz; Fedrizzi, Szymon; Kuliga, Michał; Frączkiewicz, Mateusz; Malarz, Mirosław; Puchalski, Jarosław; Danysz, Ewa; Grajcarek, Maciej