Przeglądaj wg Autor "Duraj, Agnieszka"
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Pozycja Application of FussionClassify for data classification(Wydawnictwo Politechniki Łódzkiej, 2013) Duraj, AgnieszkaIn the published articles and works there are solutions regarding data fusion. However, there is not any verification as for the efficiency of classifiers in the case of many sources of data given simultaneously. It is, seemingly, a very significant problem to be considered in the case of e.g. data fusion in intelligent traffic control. The intention of the author is to prepare the tools for classification of data which come from various sources. They can be sets (data files) prepared by the user of application, but they can also be one (or many) sets from the UCI machine learning repository (http://archive.ics.uci.edu/ml/).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, PiotrIn 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 Outlier Detection Using the Multiobjective Genetic Algorithm(Wydawnictwo Politechniki Łódzkiej, 2017) Duraj, Agnieszka; Chomątek, ŁukaszSince almost all datasets may be affected by the presence of anomalies which may skew the interpretation of data, outlier detection has become a crucial element of many datamining applications. Despite the fact that several methods of outlier detection have been proposed in the literature, there is still a need to look for new, more effective ones. This paper presents a new approach to outlier identification based on genetic algorithms. The study evaluates the performance and examines the features of several multiobjective genetic algorithms.Pozycja TEWI 2021 (Technology, Education, Knowledge, Innovation)(Lodz University of Technology Press, 2021) Wojciechowski, Adam (Ed.); Napieralski, Piotr (Ed.); Lipiński, Piotr (Ed.); Lodz University of Technology. Faculty of Technical Physics, Information Technology and Applied Mathematics Institute of Information Technology.; Byczkowska-Lipińska, Liliana; Napieralska-Juszczak, Ewa; Duraj, Agnieszka; Guskos, Andreas; Poniszewska-Marańda, Aneta; Puchała, Dariusz; Mielczarek, Jakub; Wosiak, AgnieszkaThe monograph TEWI 2021 is a direct response to the demand of the industry, which looks for research projects partners. On the one hand, the material submitted for this monograph will give opportunity for the academic community to present their competencies in the thematic areas of research they conduct. On the other hand, the industry will stimulate the research development of academic staff by outlining current and future areas in the field of modern technologies. The aim of this monograph is to popularize research areas in: 1. technology (information technologies), 2. education (new methods and IT solutions implemented in education), 3. knowledge (practical applications of physics and mathematics in technical sciences), 4. innovation (transfer of new ideas between science and business). [...]