Understanding today's cybersecurity challenges


Cryptocurrency mining infographic and e-book

University students are exploiting free electricity on campus to do cryptomining while others become unsuspecting victims by visiting nefarious websites that take over their devices to process cryptocurrency hashes.

Six critical attack vectors to detect in your data center and private cloud

Today, data center security focuses mainly on protecting the virtualized layers, which has prompted professional cybercriminals to attack the data center's physical infrastructure. However, advanced detection models can expose attacks against the data center's underlying infrastructure as well as its virtualized layers.

Minding the cybersecurity gap

The cybersecurity gap exists between the time an attacker successfully evades prevention security systems at the perimeter and the clean-up phase when an organization discovers that key assets have been stolen or destroyed. And the risks of the cybersecurity gap are big and only getting bigger.

A new threat detection model that closes the cybersecurity gap

This e-book explains the requirements for an advanced threat detection model that identifies active cyberattacks based on what has been learned from the past as well as local context. This new model then connects events over time to reveal the progression and actions of threats inside of networks.

How cyberattackers evade threat signatures

Although signatures can stop known threats, the most dangerous ones have yet to be captured and mapped. The signature model has multiple blind spots that can leave your network vulnerable to cyberattackers. Understanding these blind spots requires understanding the weakness behind signatures.


2018 RSA Conference Edition of the Attacker Behavior Industry Report

Cyberattack detections and trends from 246 Vectra customers in 14 industries and over 4.5 million devices and workloads. The report also shows a stunning surge in cryptocurrency mining in higher education.

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How to interpret network-based malware detection

This research paper examines the ecosystem nuances of network-based malware detection and the limits imposed on intelligence extraction of captured malware samples. It also explains the impact on organizations that strive to mitigate malware threats using network-based detection systems.