Detection of Anomalies in Data Streams Using the LSTM-CNN Model
This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models in the...
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MDPI AG
2025-03-01
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| author | Agnieszka Duraj Piotr S. Szczepaniak Artur Sadok |
| author_facet | Agnieszka Duraj Piotr S. Szczepaniak Artur Sadok |
| author_sort | Agnieszka Duraj |
| collection | DOAJ |
| description | This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models in the literature, which are the basis for this analysis, are the following: the LSTM and its more complicated variant, the LSTM autoencoder. Additionally, the usefulness of an innovative LSTM-CNN approach is evaluated. The results indicate that the LSTM-CNN approach can successfully be applied for anomaly detection in data streams as its performance compares favorably with that of the two mentioned standard models. For the performance evaluation, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mrow><mn>1</mn><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></msub></semantics></math></inline-formula> is used. |
| format | Article |
| id | doaj-art-104dbfbc8193412ea73af6e5cb7f0d9a |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-104dbfbc8193412ea73af6e5cb7f0d9a2025-08-20T02:58:57ZengMDPI AGSensors1424-82202025-03-01255161010.3390/s25051610Detection of Anomalies in Data Streams Using the LSTM-CNN ModelAgnieszka Duraj0Piotr S. Szczepaniak1Artur Sadok2Institute of Information Technology, Lodz University of Technology, al. Politechniki 8, 93-590 Łódź, PolandInstitute of Information Technology, Lodz University of Technology, al. Politechniki 8, 93-590 Łódź, PolandNESC Sp. z o.o., ul. Wojskowa 6/B1, 60-792 Poznań, PolandThis paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models in the literature, which are the basis for this analysis, are the following: the LSTM and its more complicated variant, the LSTM autoencoder. Additionally, the usefulness of an innovative LSTM-CNN approach is evaluated. The results indicate that the LSTM-CNN approach can successfully be applied for anomaly detection in data streams as its performance compares favorably with that of the two mentioned standard models. For the performance evaluation, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mrow><mn>1</mn><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></msub></semantics></math></inline-formula> is used.https://www.mdpi.com/1424-8220/25/5/1610outliersanomaliesdeep learning methodsLSTM |
| spellingShingle | Agnieszka Duraj Piotr S. Szczepaniak Artur Sadok Detection of Anomalies in Data Streams Using the LSTM-CNN Model Sensors outliers anomalies deep learning methods LSTM |
| title | Detection of Anomalies in Data Streams Using the LSTM-CNN Model |
| title_full | Detection of Anomalies in Data Streams Using the LSTM-CNN Model |
| title_fullStr | Detection of Anomalies in Data Streams Using the LSTM-CNN Model |
| title_full_unstemmed | Detection of Anomalies in Data Streams Using the LSTM-CNN Model |
| title_short | Detection of Anomalies in Data Streams Using the LSTM-CNN Model |
| title_sort | detection of anomalies in data streams using the lstm cnn model |
| topic | outliers anomalies deep learning methods LSTM |
| url | https://www.mdpi.com/1424-8220/25/5/1610 |
| work_keys_str_mv | AT agnieszkaduraj detectionofanomaliesindatastreamsusingthelstmcnnmodel AT piotrsszczepaniak detectionofanomaliesindatastreamsusingthelstmcnnmodel AT artursadok detectionofanomaliesindatastreamsusingthelstmcnnmodel |