siddhartb_

Joined 6 years ago

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91

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38

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Apr 7

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Recent submissions

Adobe Releases Open Source Anomaly Detection Tool github.com

1 siddhartb_ 5 years ago 0 comments

Adversarial Generation of Extreme Samples in Constant Time aihub.org

2 siddhartb_ 5 years ago 0 comments

Comment · 6 years 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 better. We are currently working towards building a semi-supervised approach for anomaly detection in real-time.

To the best of my knowledge, MStream and MIDAS are the fastest and detect anomalies in real-time.

Comment · 6 years 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 a multi-aspect data setting i.e., entries having multiple dimensions such as event-log data, multi-attributed graphs etc. MStream is two orders of magnitude faster while achieving higher accuracy on several publicly available datasets.

Github Repository: https://github.com/Stream-AD/MStream

Comment · 6 years 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 desired extremeness measure, as well as the desired extremeness probability they wish to sample at. Generating increasingly extreme examples can be done in constant time (with respect to the extremeness probability), as opposed to the exponential time required by the baseline.

Paper: https://arxiv.org/abs/2009.08454

Show HN: ExGAN-Adversarial Generation of Extreme Samples github.com

54 siddhartb_ 6 years ago 2 comments

Comment · 6 years 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.

Comment · 6 years 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.

Comment · 6 years 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 suspicious similar group of edges in time-evolving/dynamic graphs.

Comment · 6 years 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 paper here: https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf Let me know if you need more details.

Comment · 6 years ago

Code is quite neat! What are the changes it will need for including fit and predict API?