MIxmode Blog

The latest stories on Cybersecurity, AI, and everything in between from MixMode

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

By Ana Mezic | December 5, 2019

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.

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How to Use Your Baseline for Network Security

By Ana Mezic | December 3, 2019

This is the final article in a three-part series on Network Baselining. Read the first two articles in the series here:
1) An Introduction to Baselining Technology
2) How to Create a Baseline for Your Network

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How to Create a Baseline for Your Network

By Ana Mezic | November 26, 2019

We separate the parts that are wavelike and we separate the parts that are stochastic. Then, five minutes later we look again and compare what we’ve seen historically and what the AI has predicted to happen to the current state of the network. If they are different, that’s an anomaly detected.

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

By Russell Gray | November 21, 2019

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.

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An Introduction to Baselining Technology

By Ana Mezic | November 19, 2019

This is the first installment of the three part blog series on creating baselines of network behavior to improve your security stance. Here we will walk you through the basics of baselining technology.

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Multi-Stream Cybersecurity and How it Can Save Your Business from a Zero-Day Attack

By Ana Mezic | November 12, 2019

The best way to detect threats across an entire network in the quickest manner is to have complete visibility over every part of that network with a multi-stream platform which can incorporate not only network data, but Cloud Data and SIEM logs as well.

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MixMode On-Premise Now Available

By Jenny Sassi | November 5, 2019

The most popular form of in house deployment of MixMode is to deploy our OVA virtual appliance on an existing internal VMware ESXi infrastructure.

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Dynamic and Flexible AI for Network Security

By Christian Wiens | October 31, 2019

Third-Wave artificial intelligence (also known as Wave 3 AI), is making life a whole lot easier for security systems administrators. It seems like we just passed the milestones of incorporating AI into network security. But Dr. Igor Mezic and others in this field have made huge strides in the last year. In his new whitepaper on AI for Network Security, Dr. Mezic describes how Third-Wave AI brings flexibility and intuition into the world of machine learning.

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Leveraging MixMOde to monitor AWS Cloudtrail

By Russell Gray | October 29, 2019

We ingest your CloudTrail logs into our platform, apply a layer of our Proprietary Artificial Intelligence to give you advanced anomaly detection and alerting, correlate those anomalies with your underlying network data and give you access to forensic search and investigation of these logs.

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Top Security Pain Points Revealed: Building Baselines, Cloud, and Visibility Among Concerns

By Christian Wiens | October 22, 2019

Effective modern network security needs to defend against an unprecedented number of threats. Today’s SecOps teams face both rudimentary hacking attempts and highly sophisticated, targeted attacks that pose serious safety and security risks.

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About MixMode

MixMode is the first to bring a third-wave, context-aware AI approach that automatically learns and adapts to dynamically changing environments. MixMode’s monitoring platform, PacketSled, better understands network behavior as it adapts to baseline changes and enables both misuse detection and anomaly detection, as well as predictive maintenance. Used by enterprises and MSSPs for real-time network analysis, threat hunting and incident response, the platform leverages continuous stream monitoring and retrospection to provide network forensics and security analytics. Security teams can integrate PacketSled into their orchestration engine, SIEM, or use PacketSled independently to dramatically reduce false positive alerts and the resources required to respond to persistent threats, malware, insider attacks and nation state espionage efforts.

The company has been named an innovator in leading publications and by security analysts, including SC Magazine, earning a finalist award in 2018 and 2019 for "Best Computer Forensic Solution.” Based in Santa Barbara, with offices in San Diego, the company is backed by Keshif Ventures and Blu Venture Investors. For case studies, continuous product updates and industry news, please visit us at www.mixmode.ai.