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- W4285100803 abstract "Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive adversarial attacks. A malicious intrusion, defined as an invasive action in-tending to illegally exploit private resources, manifests through unusual data traffic and/or abnormal connectivity pattern. Despite the plethora of statistical or signature-based detectors currently provided in the literature, the topological connectivity component of a malicious flow is less exploited. Furthermore, a great proportion of the existing statistical intrusion detectors are based on supervised learning, that relies on labeled data. By viewing network flows as weighted directed interactions between a pair of nodes, in this paper we present a simple method that facilitate the use of connectivity graph features in unsupervised anomaly detection algorithms. We test our methodology on real network traffic datasets and observe several improvements over standard AD." @default.
- W4285100803 created "2022-07-14" @default.
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- W4285100803 date "2022-06-16" @default.
- W4285100803 modified "2023-09-27" @default.
- W4285100803 title "Unsupervised Abnormal Traffic Detection through Topological Flow Analysis" @default.
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- W4285100803 doi "https://doi.org/10.1109/comm54429.2022.9817285" @default.
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