Network Detection and Response

Why a Platform With a Generative Baseline Matters

MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.

Why The Future of Cybersecurity Needs Both Humans and AI Working Together

A recent WhiteHat Security survey revealed that more than 70 percent of respondents cited AI-based tools as contributing to more efficiency. More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.

NTA and NDR: The Missing Piece

Most SIEM vendors acknowledge the value of network traffic data for leading indicators of attacks, anomaly detection, and user behavior analysis as being far more useful than log data. Ironically, network traffic data is often expressly excluded from SIEM deployments, because the data ingest significantly increases the required data aggregation and storage costs typically 3-5x.

Whitepaper: Self-Supervised Learning – AI For Complex Network Security

Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …

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Encryption = Privacy ≠ Security

For the past few years, many have been talking about the changing “threat landscape” as it pertains to the increase in zero day, insider and phishing threats. While all of these threats are on the rise, and constitute a concern, there is, perhaps, an even larger shift presenting a threat to enterprises – the shift …

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New Video: Why is network data the best source for actionable data in cybersecurity?

In a recent blog post, our Head of Customer Success, Russell Gray, outlined the reasons why network data is the best source for actionable data in cybersecurity. He covered the limitations of each of the elements of a typical security stack (SIEM, Endpoint, and Firewall) and the importance of network traffic analysis (NTA) in the …

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3 Cyberthreats Facing Federal and State Governments in 2020

Bad actors do not discriminate. Organizations across all sectors are at risk — corporations, non-profits, and increasingly, federal and state government entities. The U.S. Government Accountability Office (GAO) reported that security incidents increased by 1,300 percent from 2006 to 2015. This number is growing.

Our Top 5 Cybersecurity Insights from 2019

This year on the MixMode blog, we have covered headline stories, analyzed every pain point within network security, and shared what we believe to be some of the most innovative solutions to help you analyze network traffic, surface threats and anomalies, and stop attacks using autonomous AI.

What Trends Will Shape the Cybersecurity Industry in 2020?

In this environment, it’s no surprise that U.S. CEOs rated cybersecurity as their top external concern in a survey conducted by the Conference Board. Those worries are unlikely to fade anytime soon, but 2020 also brings fresh opportunities for proactive measures to secure sensitive information. Here’s what you need to know about the trends that are currently emerging in cybersecurity and how you can make a difference in the future of the field:

The Evolution of “Next-Generation” Manufacturing and the Need for Network Security

The new MixMode & RAVENii whitepaper, “The Evolution of ‘Next-Generation’ Manufacturing and the Need for Network Security,” is a comprehensive look at how third-wave AI is improving modern network security across connected manufacturing networks and beyond.

Generative Unsupervised Learning vs. Discriminative Clustering Technology: Which Prevents Zero-Day Attacks?

Knowing the difference between Discriminative and Generative Unsupervised Learning can tell you a lot about the effectiveness of a cybersecurity solution’s artificial intelligence, for example, whether or not that security solution can perform actions like identifying and stopping a zero-day attack.