Summary of Adaptive Anomaly Detection in Network Flows with Low-rank Tensor Decompositions and Deep Unrolling, by Lukas Schynol et al.
Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling
by Lukas Schynol, Marius Pesavento
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of anomaly detection in network flows using incomplete measurements. The authors propose a robust tensor decomposition approach and deep unrolling techniques to address concerns about training data efficiency, domain adaptation, and interpretability. They develop a novel block-successive convex approximation algorithm based on a regularized model-fitting objective, which is extended to perform online adaptation to per-flow and per-time-step statistics. To optimize the deep network weights for detection performance, the authors employ a homotopy optimization approach based on an efficient approximation of the area under the receiver operating characteristic curve. The proposed architecture outperforms reference methods in extensive experiments on synthetic and real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to detect problems in computer networks using incomplete information. It uses special math techniques to make sure the system is reliable and can adapt to different situations. They develop a new way of training the system that works well with limited data and doesn’t require a lot of adjustments. The results show that their method performs better than others and is good at finding problems in real-world networks. |
Keywords
» Artificial intelligence » Anomaly detection » Domain adaptation » Optimization
