Integrating Anomaly Detection for Enhanced Data Protection in Cloud-Based Applications
Ładowanie...
Data
2023
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
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.
Opis
Słowa kluczowe
computer games, artificial intelligence, gry komputerowe, sztuczna inteligencja
Cytowanie
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.