Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
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Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#2Hey there. Stepan, co-founder of Wallarm, here. Feel free to ask any questions.
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#3Hey there. Stepan, co-founder of Wallarm, here. Feel free to ask any questions.
What kind of machine learning techniques do you use to generate your results?
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#4Hey there. Stepan, co-founder of Wallarm, here. Feel free to ask any questions.
I'm glad to see innovation in this area. I have a few questions.
Can you tell me where (or even how) you acquired the data to train your machine learning system?
If you could go into some detail about the specific techniques you've used that would also be great to know.
Finally, what does your service do that is not provided by something like SiftScience? I imagine there is overlap here - is it that you primarily focus on web application security instead of fraud signaling?
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#5Hey there. Stepan, co-founder of Wallarm, here. Feel free to ask any questions.
What kind of machine learning techniques do you use to generate your results?
Ivan, co-founder of Wallarm, here.
There are few different tasks for machine learning.
1. Traffic clustering (hierarchical clustering algorithms). We use ML to understand how your application works in terms of business logic. E.g. clustering numbers of HTTP requests for /login as cluster determined by (HTTP_header->HOST="yoursite.com" + HTTP_URL->"/login" + ...).
2. Data profiling inside clusters. We use statistical distribution algorithms to understand which data is normal for fields POST->login and POST->password inside cluster from p.1. It is not hardcoded data templates like "only digits" or smth like this. Wallarm generates profiles dynamically.
3. Fuzzy search. Those data which is abnormal (from p.2), we understand if it looks like XSS or SQLi or any other attack or not.
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#6Hey there. Stepan, co-founder of Wallarm, here. Feel free to ask any questions.
I'm glad to see innovation in this area. I have a few questions. Can you tell me where (or even how) you acquired the data to train your machine learning system? If you could go into some detail about the specific techniques you've used that would also be great to know. Finally, what does your service do that is not provided by something like SiftScience? I imagine there is overlap here - is it that you primarily foc…
CEO and cofounder of Sift Science here. I think we are complementary, actually. Wallarm focuses on security vulnerabilities (like a more automated HackerOne), and we focus more on "application abuse" (user-level fraud).
Great job, Wallarm!
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#7Earlier quoted context omitted.
I'm glad to see innovation in this area. I have a few questions. Can you tell me where (or even how) you acquired the data to train your machine learning system? If you could go into some detail about the specific techniques you've used that would also be great to know. Finally, what does your service do that is not provided by something like SiftScience? I imagine there is overlap here - is it that you primarily foc…
CEO and cofounder of Sift Science here. I think we are complementary, actually. Wallarm focuses on security vulnerabilities (like a more automated HackerOne), and we focus more on "application abuse" (user-level fraud). Great job, Wallarm!
That's what I figured, I was just hoping to get a bit more nuance on the security vulnerabilities.
I actually thought Hackerone or Bugcrowd would be first to market with a product like this.
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#8Hey there. Stepan, co-founder of Wallarm, here. Feel free to ask any questions.
I'm glad to see innovation in this area. I have a few questions. Can you tell me where (or even how) you acquired the data to train your machine learning system? If you could go into some detail about the specific techniques you've used that would also be great to know. Finally, what does your service do that is not provided by something like SiftScience? I imagine there is overlap here - is it that you primarily foc…
Thanks for feedback!
1. Customers analyze traffic with locally installed NGINX-based instances (there is not DNS take-over). They send applications/traffic statistics to Wallarm Cloud so we can run machine-learning stuff. We had a lot of work done for initial training of the system using our own experience in web app security (more than 250+ pentests for top-tier companies + a lot of researches done by our team like SSRF bible). We also use different honeypots and now statistics of customers with a high volume traffic.
2. There are some details about ML technique covered by Ivan for another comment
3. We have different tasks with SiftScience. SiftScience provides a fraud-detection. Wallarm protects web apps and APIs against data breaches. But these tasks are related for some of our customers.
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#9Earlier quoted context omitted.
CEO and cofounder of Sift Science here. I think we are complementary, actually. Wallarm focuses on security vulnerabilities (like a more automated HackerOne), and we focus more on "application abuse" (user-level fraud). Great job, Wallarm!
That's what I figured, I was just hoping to get a bit more nuance on the security vulnerabilities. I actually thought Hackerone or Bugcrowd would be first to market with a product like this.
Hackerone and Bugcrowd do a great job. And we recommmend to run bug-bounty programs all the time.
But companies which run fast and deploy code everyday with CI/CD (or several times a day) it's almost impossible not to introduce new vulnerabilities. This is where solutions for continuous security are incredibly helpful.
Re: Wallarm (YC S16) Uses Incoming Hacker Attacks to Reveal Security Flaws
#10Earlier quoted context omitted.
I'm glad to see innovation in this area. I have a few questions. Can you tell me where (or even how) you acquired the data to train your machine learning system? If you could go into some detail about the specific techniques you've used that would also be great to know. Finally, what does your service do that is not provided by something like SiftScience? I imagine there is overlap here - is it that you primarily foc…
CEO and cofounder of Sift Science here. I think we are complementary, actually. Wallarm focuses on security vulnerabilities (like a more automated HackerOne), and we focus more on "application abuse" (user-level fraud). Great job, Wallarm!
Thanks Jason! Will be at DEFCON this year? It'll be great to meet there.