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How a Massive Shift to Working From Home Leaves an Enterprise’s Cybersecurity Vulnerable

Blog, Network Detection and Response, Network Security, Network Traffic Analysis, Threat Detection, Uncategorized, Zero Day Attacks / By

Many companies are scrambling to find a way to better protect their now-remote team of employees, and as they do so, hackers will take every advantage to find the weaknesses in these spread-out company networks.

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Whitepaper: Actionable Anomalies – How MixMode AI Makes Your Security Data Smarter

Blog, Network Baselines, Network Security, Network Traffic Analysis, Unsupervised AI, Zero Day Attacks / By

In today’s ever evolving cybersecurity landscape there are major problems facing professionals that continue to worsen. These problems center around a shortage of tools advanced enough to understand the baseline of a network in order to pinpoint anomalies and a massive information overload problem in the form of security alerts.

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Staying CCPA Compliant with MixMode’s Unsupervised AI

Blog, Data Privacy, Network Baselines, Network Detection and Response, Network Security, Regulations & Compliance, Threat Detection, Unsupervised AI, Zero Day Attacks / By

Companies are expected to spend up to $55 billion dollars on efforts to comply with the California Consumer Privacy Act (CCPA), which is still working out its final rules after going into effect this month.

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Yesterday’s SIEM Solutions Can’t Combat Today’s Cyberthreats

Blog, Network Detection and Response, Threat Detection, Unsupervised AI, Zero Day Attacks / By

While it’s true that having a SIEM is better than forgoing network monitoring all together, a standalone SIEM solution is simply insufficient in today’s cybersecurity landscape. Hackers and other bad actors have become more sophisticated — many of today’s cybercriminals can easily outsmart a standard SIEM setup.

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Don’t Fall for the Hype – Marketing Myths in Artificial Intelligence for Cybersecurity

Blog, Product Updates, Unsupervised AI / By

The cybersecurity provider landscape is cluttered with impossible claims, misrepresentations, and a confusing mix of inconsistent terminology. Worse, every minute you delay making a decision is another minute hackers have to gain access and knowledge about your network.

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

Blog, Network Detection and Response, Threat Detection, Unsupervised AI, Zero Day Attacks / By

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.

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

Case Study: MixMode AI Detects Attack not Found on Threat Intel

Blog, Case Study, Network Detection and Response, Threat Detection / By

In October, 2019 a MixMode customer experienced an incident where an external entity attacked a web server located in their DMZ, compromised it, and then pivoted internally through the DMZ to attempt access of a customer database. While the attacker was successful in penetrating the customer’s network, MixMode was able to detect the event before they were successful in penetrating the customer database.

Case Study: MixMode AI Detects Attack not Found on Threat Intel Read More →

The Difference Between Artificial Intelligence and Machine Learning in Network Security

Blog, Unsupervised AI / By

Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably when discussing developments in deep learning. However, there is an important difference between the two that network security professionals will need to understand in order to serve their clientele effectively.

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