Presented at
DeepSec 2017 „Science First!“,
Unknown date/time
(Unknown duration)
A lot of research was done about clustering attacks of different types using many Machine Learning algorithms, with high rates of success. These were mainly done from the comfort of a research lab, with specific datasets and no performance limitations.
In this session I will share my experience with dealing with clustering of attacks in near real-time scenarios where performance is a key factor, and where the reality punches lab statistics in the face.
I will discuss some of the challenges we experienced during the research like:
1) Applying a clustering algorithm to a stream of data.
2) Extracting meaningful features from limited data.
3) Translating different features into something we can calculate distance from.
Presenters:
-
Gilad Yehudai
- Imperva
Gilad Yehudai is an algorithm developer and security researcher at Imperva's web application research group. Gilad develops algorithms and solutions using state-of-the-art machine learning algorithms, and also researches new security threats and vulnerabilities.
Gilad holds a B.Sc. and a M.Sc. in Mathematics from Tel Aviv University. He has a very analytical and technical background with experience in both statistics and machine learning. A math geek by day and an avid Snooker player by night (And vice versa).
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