siddharthbhatia

4 ms·

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by siddharthbhatia 7y ago
Hi, here is the MIDAS github repository: https://github.com/bhatiasiddharth/MIDAS Few use cases include detecting intrusions, fake ratings and financial fraud.
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Controlling Fake News Using Graphs and Statistics(towardsdatascience.com)
24 points by siddharthbhatia 7y ago|2 comments
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by siddharthbhatia 7y ago
See https://github.com/yzhao062/pyod for Python Its quite good.
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Show HN: Anomaly Detection in Ruby(github.com)
24 points by siddharthbhatia 7y ago|6 comments
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Streaming Anomaly Detection in Ruby(github.com)
3 points by siddharthbhatia 7y ago|0 comments
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by siddharthbhatia 7y ago
In Figure 7 of the paper, we show an example of detection when neither edge/source/destination is individually anomalous but as a whole, it is a microcluster anomaly. It can similarly be detected when there are multiple web-hostin
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by siddharthbhatia 7y ago
True
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by siddharthbhatia 7y ago
We have extended the work for multi-aspect data i.e. records with multiple features. Currently our approach is capable of handling structured data only. It should be interesting to see how to detect anomalies using this approach in data whi
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by siddharthbhatia 7y ago
We handle spatial locality in terms of not just the source but also the destination, therefore we should be able to handle DDoS like attacks when simultaneous edges come from several sources trying to deny one particular destination.
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by siddharthbhatia 7y ago
Thank you :) 1. We use a temporal decay (alpha). 2. Good question! We consider similar edges as those having at least one of source and destination node as the same. 3. Very interesting direction for future work! We can try using a variable
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by siddharthbhatia 7y ago
Thanks! There is a subtle difference. We define microclusters within the category of anomalous edge detection as 'suddenly arriving groups of suspiciously similar edges' e.g. denial of service attacks in network traffic data and l
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by siddharthbhatia 7y ago
Anomaly detection in graphs is a critical problem for finding suspicious behavior in innumerable systems, such as intrusion detection, fake ratings, and financial fraud. But most of the systems in place focus either on static graphs or on e
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Show HN: Fast Real-Time Anomaly Detection in Dynamic Graphs(github.com)
118 points by siddharthbhatia 7y ago|22 comments
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Show HN: Real-Time Anomaly Detection in Graphs(github.com)
4 points by siddharthbhatia 7y ago|0 comments
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Show HN: Streaming Anomaly Detector(github.com)
1 points by siddharthbhatia 7y ago|0 comments
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Show HN: Midas, a Streaming Anomaly Detector(github.com)
11 points by siddharthbhatia 7y ago|0 comments
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by siddharthbhatia 7y ago
This is very true. LinkedIn has just been trying to mint money from the very people who helped build the company!
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by siddharthbhatia 7y ago
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually
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A Microcluster-Based Anomaly Detector in Edge Streams(github.com)
4 points by siddharthbhatia 7y ago|1 comments
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DeepMind founder Mustafa Suleyman leaves indefinitely(globalone.com.np)
2 points by siddharthbhatia 7y ago|0 comments
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by siddharthbhatia 7y ago
Arxiv paper link: https://arxiv.org/abs/1911.04464
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by siddharthbhatia 7y ago
MIDAS detects microcluster anomalies from an edge stream in constant time and memory, while providing theoretical guarantees about its false positive probability. Microcluster anomalies are suddenly arriving groups of suspiciously similar e
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Show HN: Midas, Microcluster-Based Detector of Anomalies in Edge Streams(github.com)
15 points by siddharthbhatia 7y ago|2 comments