Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network

Date

2021-01-01

Author

Tufan, Emrah
Tezcan, Cihangir
Acartürk, Cengiz

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Cyber attacks constitute a significant threat to organizations with implications ranging from economic, reputational, and legal consequences. As cybercriminals' techniques get sophisticated, information security professionals face a more significant challenge to protecting information systems. In today's interconnected realm of computer systems, each attack vector has a network dimension. The present study investigates network intrusion attempts with anomaly-based machine learning models to provide better protection than the conventional misuse-based models. Two models, namely an ensemble learning model and a convolutional neural network model, were built and implemented on a data set gathered from a real-life, institutional production environment. To demonstrate the models' reliability and validity, they were applied to the UNSW-NB15 benchmarking data set. The type of attack was limited to probing attacks to keep the scope of the study manageable. The findings revealed high accuracy rates, the CNN model being slightly more accurate.

URI

https://hdl.handle.net/11511/89925

Journal

IEEE ACCESS

DOI

https://doi.org/10.1109/access.2021.3068961

Collections

Graduate School of Informatics, Article

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Citation Formats

E. Tufan, C. Tezcan, and C. Acartürk, “Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network,” IEEE ACCESS, pp. 50078–50092, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89925.