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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

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20232

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Ma przypisane plikisearch.filters.applied.operator.equals: TakTematsearch.filters.applied.operator.equals: search.filters.subject.computer games

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Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    Learning Non-Differentiable Graphs of Utility AI
    (Wydawnictwo Politechniki Łódzkiej, 2023) Świechowski, Maciej
    Utility AI is an approach to modelling AI players in computer games. Its structure is a graph that computes the utility values of possible actions and chooses the one with the highest value. Currently, such graphs are created by experts manually. This paper presents the first attempts to create them automatically – through learning from data. The problem is similar to training neural networks except that the utility graphs are non-differentiable and contain various types of nodes (more complex than neurons). We present the most promising methods, preliminary experiments and results.
  • 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.