siddhartb_

7 ms·

1.
Adobe Releases Open Source Anomaly Detection Tool(github.com)
1 points by siddhartb_ 5y ago|0 comments
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Adversarial Generation of Extreme Samples in Constant Time(aihub.org)
2 points by siddhartb_ 6y ago|0 comments
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by siddhartb_ 6y ago
MStream and MIDAS are more accurate than previous baselines for unsupervised anomaly detection. However, there can be scenarios where some labels (ground truth information) are known. In such cases, a semi-supervised algorithm might work be
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by siddhartb_ 6y ago
Hi, I am one of the authors of the work. MStream detects anomalies, intrusions, DoS and DDoS attacks in real time and constant memory. It is built on top of MIDAS ( https://github.com/Stream-AD/MIDAS/ ) and works in
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by siddhartb_ 6y ago
Existing GAN based approaches excel at generating realistic samples, but seek to generate typical samples, rather than extreme samples. We propose ExGAN to generate realistic and extreme samples. ExGAN allows the user to specify both the de
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Show HN: ExGAN-Adversarial Generation of Extreme Samples(github.com)
54 points by siddhartb_ 6y ago|2 comments
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by siddhartb_ 6y ago
Extreme Value Theory is extensively used in anomaly detection as well. This can be a first step towards generating anomalous data which is usually quite difficult to find.
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by siddhartb_ 6y ago
There is a recorded presentation of the paper at https://youtu.be/Bd4PyLCHrto The first 5-10 minutes or so should be quite explanatory. Please feel free to let me know if you have any specific doubts. Thanks.
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by siddhartb_ 6y ago
Detecting Intrusions, Denial of Service (DoS) attacks, Distributed Denial of Service (DDoS) attacks. It can also be used to detect fake profiles in Social Networks like Twitter, Facebook, Amazon reviews, and Financial Frauds. Basically any
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by siddhartb_ 6y ago
Hi, I'm the author of the MIDAS algorithm. We choose the number of hash functions and bucket according to the maximum error we can tolerate and the false positive probability theoretical guarantee we want. Please refer to the AAAI pape
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by siddhartb_ 6y ago
Code is quite neat! What are the changes it will need for including fit and predict API?
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by siddhartb_ 6y ago
Yes, there is a Python implementation available. MIDAS has also been converted to Rust and Ruby. Please check out the Github page for the links.
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by siddhartb_ 6y ago
This is an R wrapper around the C++ implementation https://github.com/bhatiasiddharth/MIDAS MIDAS can help social networks like Twitter and Facebook detect fake profiles used for spam and phishing in real-time, at a sp
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R Package to Control Fake News in Twitter and Facebook(cran.r-project.org)
10 points by siddhartb_ 6y ago|3 comments
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by siddhartb_ 6y ago
Great question, it will be interesting to try it out. Temporal relations should be affected a bit but MIDAS should be able to detect anomalies.
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by siddhartb_ 6y ago
In addition to detecting intrusions, it can detect fake ratings and frauds. Basically finding anomalous and suspicious behavior in any dynamic (time-evolving) graph.
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by siddhartb_ 6y ago
Yes, we take expected count of a particular user/source node into consideration.
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by siddhartb_ 6y ago
We handle locality in terms of both source and destination, therefore we should be able to handle both DoS and DDoS attacks.
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by siddhartb_ 6y ago
Code is available in C++, Python, Ruby, R, and Rust at https://github.com/bhatiasiddharth/MIDAS
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Show HN: Intrusion Detection in Real-time(arxiv.org)
69 points by siddhartb_ 6y ago|11 comments
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by siddhartb_ 6y ago
Currently MIDAS is available in Rust, Python, Ruby and R at https://github.com/bhatiasiddharth/MIDAS . If someone is interested to convert MIDAS to other languages, please feel free to do so and let me know so that I ca
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by siddhartb_ 6y ago
We assume (like any anomaly detection algorithm) that the majority is normal sample. In your context, the normal samples will be considered as outliers and therefore caught by the algorithm. One way to mitigate this is to either swap the la
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by siddhartb_ 6y ago
Definitely. It will need only very small changes to the code. I would love to add it as a plugin. Can you point to some resources that can help in incorporating MIDAS into Gephi.
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by siddhartb_ 6y ago
Sounds interesting. Can you elaborate on what the data is like?
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by siddhartb_ 6y ago
Interesting question. With an increase in dimensions, we consider the correlation between the features in addition to considering them individually. The work is currently under review. Feel free to get in touch and I can update you once we
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by siddhartb_ 6y ago
Nice suggestion. Will definitely try to refactor. Thanks! In most of the cases, timestamps should be with the data itself (assuming its a dynamic graph). If timestamps are to be chosen, one can select in a way seeing how many edges usually
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by siddhartb_ 6y ago
Thanks, MIDAS can be used to detect intrusions, fake ratings, frauds. Basically finding anomalous and suspicious behavior in a dynamic (time-evolving) graph. We have also extended MIDAS to detect group anomalies in higher-dimensional record
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by siddhartb_ 6y ago
We detect suddenly appearing bursts of activity which share many repeated nodes or edges, which we refer to as microclusters. E.g. denial of service (DoS) attacks in network traffic data and lockstep behavior. Also, we detect scenarios wher
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by siddhartb_ 6y ago
Thanks. We give theoretical guarantees on the False Positive Probability which can be useful to decide the parameters. Some use cases of the project include detecting: 1. Intrusions 2. Fake Ratings 3. Financial Fraud
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by siddhartb_ 6y ago
Code and Datasets we used are available at https://github.com/bhatiasiddharth/MIDAS

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