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Arshak Navruzyan's Blog (7)

Distillation of Deepnets

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…

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Added by Arshak Navruzyan on December 23, 2016 at 7:30am — No Comments

Probabilistic Pentesting

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…

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Added by Arshak Navruzyan on November 12, 2016 at 1:00pm — No Comments

Honeypot Turing Test

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…

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Added by Arshak Navruzyan on October 12, 2016 at 7:00pm — No Comments

Applying ML to InfoSec: Adversarial ML

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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Added by Arshak Navruzyan on July 4, 2016 at 10:00am — No Comments

Detecting Money Laundering with Unsupervised ML

Financial institutions have a regulatory requirement to monitor account activity for anti-money laundering (AML). Regulators take the monitoring and reporting requirements very seriously as evidenced by a recent set of FinCEN fines.  

One challenge with AML is that it rarely manifests as the activity of a single person, business, account, or a transaction. Therefore detection requires behavioral pattern analysis of…

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Added by Arshak Navruzyan on June 26, 2016 at 5:00am — 1 Comment

Formulation of Adversarial ML

Machine learning is being used in a variety of domains to restrict or prevent undesirable behaviors by hackers, fraudsters and even ordinary users.  Algorithms deployed for fraud prevention, network security, anti-money laundering belong to the broad area of adversarial machine learning where instead of ML trying to learn the patterns of benevolent nature, it is confronted with a malicious adversary that is looking for opportunities to exploit loopholes…

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Added by Arshak Navruzyan on May 31, 2016 at 9:00am — No Comments

Data Science Fellowship Focused on Practical Experience

You've made up your mind to become a data scientist. You've taken every data science MooC, you've eaten a lifetime of pizza at machine learning meetups, you even attended a data science "academy." Why hasn't it worked?

Data Science is not knowledge to be acquired but rather a skill that can be learned and improved through practice. The number one qualification employers look for when hiring a data science candidate is previous experience. Startup.ML is launching a fellowship to give…

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Added by Arshak Navruzyan on March 5, 2015 at 10:00am — No Comments

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