Understanding today's cybersecurity challenges

Analyst Reports

Network-based threat detection

Over time, given the significant (and likely insurmountable) security staffing constraints, organizations need to embrace automated actions based on alerts from detection.“Trustable automation” will require detection to continue to evolve in both accuracy and scale. With new technologies described in this paper, detection can make the requisite improvements to provide the basis for this critical automation.

Insider Threat Spotlight Report, 2015

The Insider Threat Spotlight Report is a survey of more than 500 cybersecurity professionals in the Information Security Community on LinkedIn, conducted to shed light on the challenges of combatting insider threats.

BYOD and Mobile Security Spotlight Report, July 2014

The BYOD Mobility & Security Spotlight Report nearly reveals that almost half of the IT organizations who responded to a recent survey are exposed to malware and embedded security exploits brought in by employees or others using downloaded apps or content on personal devices.


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 cyber criminals 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.

Five ways cybercriminals conceal command-and-control communications

Successful attacks depend on the ability to spy, spread and steal without detection. And to do that, cybercriminals employ five critical techniques to conceal their attack communications – encryption, hidden tunnels, hiding in allowed applications, external remote access tools, and anonymizing technologies.

How cyber attackers 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 cyber attackers. Understanding these blind spots requires understanding the weakness behind signatures.

IDS meets data science, machine learning and behavioral analysis

With an arsenal of complex and intelligently constructed attack methods at their disposal, today’s cybercriminals can easily outwit the traditional intrusion detection systems (IDS) that are supposed stop them. What the world needs now is a new generation of IDS that restores detections as the top priority.

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 cyber attacks 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.

What’s your ROI for cybersecurity?

IT security organizations have limited resources to address unlimited risks, threats and attackers. This means security products must be effective as well as operationally efficient. Does your security infrastructure drain manpower and resources or does it make your staff more productive and nimble?


Post-Intrusion Report, 1Q 2017

The Post-Intrusion Report reveals cyber attack detections and trends from nearly 200 Vectra enterprise customers across 13 different industries. By examining attacker behaviors, Vectra shows where potential exposure and risk exist inside networks and uncovers strong indicators of potentially damaging data breaches.

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How to interpret network-based malware detection

This research paper by Vectra CSO Günter Ollmann 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.