Arshak Navruzyan
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Distillation of Deepnets

Posted on December 23, 2016 at 7:30am 0 Comments

Training modern deepnets can take an inordinate amount of time even with the best GPU hardware available. Inception-3 on ImageNet 1000 using 8 NVIDIA Tesla K40s takes about 2 weeks (Google Research Blog).

Even when a large network is trained successfully, the memory footprint and the prediction latency (due to the number of its parameters) can make it challenging to put it into production.

One way to keep…


Probabilistic Pentesting

Posted on November 12, 2016 at 1:00pm 0 Comments

Pentesting tools like Metasploit, Burp, ExploitPack, BeEF, etc. are used by security practitioners to identify possible vulnerability points and to assess compliance with security policies. Pentesting tools come with a library of known exploits that have to be configured or customized for your particular environment.  This configuration typically takes the form of a DSL or a set of fairly complex UIs to configure individual…


Honeypot Turing Test

Posted on October 12, 2016 at 7:00pm 0 Comments

The honeypot is a method of cybersecurity in which a bait (‘honey’) system/network is designed to emulate or act as a real system/network to divert malicious attacks upon the actual real system/network.  The honeypot may act to mitigate, block, and in some cases capture the malicious behavior.  The concept of the honeypot probably originated from two books, “The Cuckoos Egg” by Clifford Stoll and “An Evening with Berferd” by Bill Chewick, both describing the authors’ own personal…


Applying ML to InfoSec: Adversarial ML

Posted on July 4, 2016 at 10:00am 0 Comments

There seems to be very little overlap currently between the worlds of infosec and machine learning. If a data scientist attended Black Hat and a network security expert went to NIPS, they would be equally at a loss. 

This is unfortunate because infosec can definitely benefit from a probabilistic approach but a significant amount of domain expertise is required in order to apply ML methods.

Machine learning practitioners face a few challenges for doing work in this domain…


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