Abstract
This paper presents an anomaly detection approach based on deep learning techniques. A bidirectional long-short-term memory (Bi-LSTM) was applied on the UNSW-NB15 dataset to detect the anomalies. UNSW-NB15 represents raw network packets that contains both the normal activities and anomalies. The data was preprocessed through data normalization and reshaping, and then fed into the Bidirectional LSTM model for anomaly detection. The performance of the BLSTM was measured based on the accuracy, precision, F-Score, and recall. The Bi-LSTM model generated high detection results compared to other machine learning and deep learning models.
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This research is based upon the work supported by the CISCO systems, Inc.
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Aljbali, S., Roy, K. (2021). Anomaly Detection Using Bidirectional LSTM. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_45
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DOI: https://doi.org/10.1007/978-3-030-55180-3_45
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