Detecting Malicious Traffic with Machine Learning

  1. Clustering data points within similar behavioral groups
  • High Data Density
  • Clear, Actionable Outcomes
  • Quantifiable Variation
Figure 1. We generate various groups with different behavior such as Inter-request time and the total number of unique endpoints (or URLs). Data points in each group exhibit similar behavior.
Figure 2. When we test a model, we apply the classifier to a new set of data. We want to answer the following questions: Did our model discover new clients that exhibit known bad behavior? Does our model detect requests by bots or valid users (humans)?

--

--

--

Formerly Verizon Media Platform, Edgecast enables companies to deliver high performance, secure digital experiences at scale worldwide. https://edgecast.com/

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

FROM OSINT PROJECT TO MAKING MILLIONS HACKING DATABASES FOR BUG BOUNTY

Governments must adopt an agile mind-set towards security

The Importance of a Cloud Security Strategy: Authentication

Pwning your assignments: Stored XSS via GraphQL endpoint

Host Your Own Site with MEW: Introducing IPFS Support for .ETH and .CRYPTO Domains

Moving to a secure systems

Privacy Debt is the New Technical Debt

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Edgecast

Edgecast

Formerly Verizon Media Platform, Edgecast enables companies to deliver high performance, secure digital experiences at scale worldwide. https://edgecast.com/

More from Medium

How Machine Learning is Improving Cybersecurity

Predict if SQL Injection Query can get access to Database.

REAL-TIME CROWD DETECTION ANALYTICS IN TRADING OUTLETS

“What’s in a Name?” Shakespeare’s age-old question answered using machine-learning