Abstract
Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly orchestrate botnets composed of simple devices. Therefore, we illustrate a solution based on Deep Learning for the detection of covert channels targeting the TTL field of IPv4 datagrams. To this aim, we take advantage of an Autoencoder ensemble to reveal anomalous traffic behaviors. An experimentation on realistic traffic traces demonstrates the effectiveness of our approach.
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Notes
- 1.
In this work, we used the collection of IoT traffic made available in [14]. Heatmaps have been computed by using the 24-h slice of data captured from September 22, 2016 at 16:00 to September 23, 2016 at 16:00, whereas for the performance evaluation we used traces containing traffic collected from September 22, 2016 at 16:00 to September 29, 2016 at 16:00.
- 2.
TensorFlow machine learning library. Available online at: https://www.tensorflow.org/ [Last Accessed: June 2022].
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Acknowledgment
This work has been partially supported by the Horizon 2020 Program within the framework of CyberSec4Europe (Grant Agreement No. 830929).
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Cassavia, N., Caviglione, L., Guarascio, M., Liguori, A., Zuppelli, M. (2022). Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_20
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