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shivin9

4 karma · joined January 27, 2020

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shivin9··on Show HN: Adversarial Generation of Extreme Samples
It is interesting to mix EVT and GANs. Importance Splitting, a statistical tool that estimates probability of rare events and generates some of them, may also be closely related.
shivin9··on Show HN: Intrusion Detection in Real-time
Can this detect DDoS like attacks also?
shivin9··on Show HN: Fast Anomaly Detection in Graphs [pdf]
Can you share the implementation?
shivin9··on Controlling Fake News Using Graphs and Statistics
Can you do some analysis for the upcoming presidential recommendations based on twitter data or something...?
shivin9··on Show HN: Fast Real-Time Anomaly Detection in Dynamic Graphs
Anything that can be modelled as a graph should work I suppose.
shivin9··on Show HN: Fast Real-Time Anomaly Detection in Dynamic Graphs
Ah alright, that makes sense. But it works only when the destination is the same. In a setting where there are multiple web-hosting servers, you would need to treat a group of source and destination points as micro-clusters themselves.

Can you extend MIDAS to adapt to that scenario?

shivin9··on Show HN: Fast Real-Time Anomaly Detection in Dynamic Graphs
Thanks. It would be nice to have similarity methods that are not dependent on spatial locality to detect DDoS like attacks.
shivin9··on Show HN: Fast Real-Time Anomaly Detection in Dynamic Graphs
Impressive results! Few questions though:-

1) Do you use sliding window approach or exponential decay?

2) When you say groups of similar edges, do they have to be spatially close to each other? or can they be equally distributed in the graph?

3) Can an attacker figure out the optimum time difference between his attacks such that MIDAS doesn't detect it as an anomaly? The time gap is just sufficient enough for the algorithm to weed out the potentially malicious micro-cluster as obsolete.

Seems like an interesting extension to your work. Best of luck!