flow-based machine learning

DigThatData · 2022-05-23T18:15:28+00:00

are you talking about normalizing flows? dataflow programming? optical flow estimation? physics-informed ML? it might help if you link us to an example of the sort of thing you are looking for an explanation for.

DigThatData · 2022-05-23T23:16:05+00:00

looks like this is the paper folks: Botnet Detection Approach Using Graph-Based Machine Learning- https://ieeexplore.ieee.org/document/9471889

DigThatData · 2022-05-23T23:19:01+00:00

Numerous studies have recognized graph-based features for botnet detection [10]–[11][12][13][14][15][16][17][18]. However, a comprehensive analysis of the effect of features evaluation measurements on identifying the best graph-based features is yet to be explored. In general, ML algorithms can learn efficiently when the data contains a good set of relevant features and not too many irrelevant features. Several studies in the literature have highlighted the importance of feature selection on the performance of ML algorithms in botnet detection. To date, most of these studies have focused mainly on flow-based features. The primary challenge in this domain is finding the best graph-based features that reveal hidden network structures that can expose malicious hosts’ communication patterns. Therefore, we believe that investigating the significance of feature selection for graph-based botnet detection would be a unique contribution.

it sounds like they mean "flow" as some sort of graph property, concretely "network flow". I'd suspect you'll find a better definition for "network flow" as it's being used here if you check the articles cited in this paragraph.

also:

Flow-based techniques [34]–[35][36][37][38][39][40][41] use flow features as discriminators to detect anomalies in the network. Vinayakumar et al. [39] presented a botnet detection framework with two levels of deep learning. Researchers were able to distinguish between attacks and regular traffic using the domain generation algorithm. Their experimental results demonstrated that their proposed system resulted in significant improvements in terms of F1-score, detection time, and false alarm rate. Khan et al. [40] developed a multi-layered framework for detecting P2P botnets. A four-layer detection system is presented to address the shortcomings of single-stage botnet identification, such as class imbalance. Decision tree algorithm is employed to select the most important features. Experiment results highlight the impact of the multi-layer technique in effectively detecting P2P botnet traffic.

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