Integrating Anomaly Detection for Enhanced Data Protection in Cloud-Based Applications


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.


Słowa kluczowe

computer games, artificial intelligence, gry komputerowe, sztuczna inteligencja


Czerkas K., Drozd M., Duraj A., Lichy K., Lipiński P., Morawski M., Napieralski P., Puchała D., Kwapisz M., Warcholiński A., Karbowańczyk M., Wosiak P., Integrating Anomaly Detection for Enhanced Data Protection in Cloud-Based Applications. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 173-179, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.27.