MIxmode Blog
MixMode Product Updates, Stories on Cybersecurity, AI, and Everything in Between.
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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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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
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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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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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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Today we announced a small but exciting update to MixMode’s Network Traffic Analysis platform! We have made performance and usability updates to the indicators page below: The Indicators page has been completely re-architected for a tremendous improvement in speed and performance. I urge you to go and try it for yourself right now – you […]
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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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Alert fatigue is a top pain point in cybersecurity right now. Security operations center (SOC) analysts are chasing an overwhelming amount of false positive alerts taking time away from analyzing and remediating actual security threats. According to a recent study…
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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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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.
Read MoreAbout MixMode
MixMode is a no-rules Cybersecurity platform, serving large enterprises with big data environments across a variety of industries. MixMode delivers a patented, self-learning platform that acts as the Intelligence Layer℠ to detect both known and unknown attacks, including novel attacks designed to bypass legacy cyber defenses. This is accomplished in real-time, across any cloud or on-premise data stream. Trusted by global entities in banking, public utilities and government sectors, industry cyber leaders rely on MixMode to protect their most critical assets. The platform dramatically improves the efficiency of SOC teams’ previously burdened with writing and tuning rules and manually searching for attacks. The MixMode platform can be deployed remotely, with no appliances, in under an hour with business outcomes evident within days. Backed by PSG and Entrada Ventures, the company is headquartered in Santa Barbara, CA. Learn more at www.mixmode.ai.