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Automated Detection & Response Blog

machine learning
Supervised and Unsupervised Machine Learning: The Yin Yang of Cybersecurity
By Daniel Felman, Lead Data Scientist, SecBI In Chinese philosophy, the Yin and Yang represent how seemingly adverse poles might actually complement one anothe...
incident response
Incident Response
By Arie Fred, VP of Product, SecBI Security incidents happen, despite the preventive technologies put in place to stop them. Thus, there is a need for an effec...
analytics
Deception vs Analytics, or Can Analytics Catch True Unknown Unknowns?
By Gilad Peleg, CEO of SecBI Gartner analyst, Anton Chuvakin, recently published a debate post, looking into deception tools as a way to detect “unknown unknow...
cybersecurity trends
Top Cyber Trend Predictions for Enterprises to Look for in 2019
  By Gilad Peleg, CEO of SecBI 1. In SaaS nobody knows you were hacked leading enterprises to seek better visibility ...
Le SIEM : ce mirage de la Sécurité informatique
  By Cyrille Herdhuin, Cyber Security professional at SCOR For the English version of the blog, click here.   Pour ceux qui ne le sauraient ...
facebook hack
Lessons learned from the Facebook breach: The Chain Reaction of Cybersecurity Ha...
  By Alex Vaystikh, Co-founder, SecBI Facebook recently suffered one of its major breaches in its history. Needless to say, if a Facebook cyberattack b...
threat detection
Living on the Defensive
  By Arie Fred, VP of Product, SecBI Whether we want to admit it or not, we as cyber defenders are always one step ...
machine learning
Pros and Cons of Unsupervised Vs Supervised Machine Learning
  By Oren Domaczewski, Product Manager, SecBI Machine learning in cyber threat detection has been hyped as the answer to increasingly ineffective signa...
Gambling with Connections
By Oren Domaczewski, Researcher, Office of the CTO Recently, a casino was reported to have been hacked, and 10 gigabytes were exfiltrated through its internet-...
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