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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.
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.
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.
Not all Artificial Intelligence is created equal. As we move towards a future where we lean on cybersecurity much more in our daily lives, it’s important to be aware of the differences in the types of AI being used for network security.
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.
The cybersecurity industry is in need of a massive shakeup. Rules-based software has created an exponential increase in false positive security alerts and zero-day threats are on the rise.
In the face of intelligent cyber threats, the best defense is to supplement traditional methods with anomaly detection through unsupervised artificial intelligence (AI).
Mixmode’s platform utilizes third-wave, context-aware AI to inform the user of threats (including Zero-Day, and encrypted traffic) on their network, and reduces the rate of false positives in intel and alerts consistently by 90% or more.
Unsupervised artificial intelligence, also known as context-aware or third-wave AI, is notoriously difficult to explain because there lacks an appropriate test to understand just how powerful the intelligence is. The widely known Turing Test for AI testing is no longer the right framework for modern-day AI testing.
From Network Detection and Response (NDR) to potentially helping to displace passwords as the user authentication standard, there are many reasons why AI is helping advance modern cybersecurity.
Recent cybersecurity news, trends, and highlights and how artificial intelligence (AI) is transforming the industry.
UK Firms Turn to AI for Help with Hacking Over half of businesses in the UK faced a hack just this year, according to Hiscox, up from 40 percent last year. The huge jump in threats is causing business owners in the UK and all over the world to start to pay close attention to …
Banks are Employing AI for Cybersecurity Needs: This Week in AI Read More →
Here’s Why It’s Time To Prepare for AI Wielding Hackers “The war against cyber attackers isn’t a fair battle. Companies have to defend against all attacks, while the attackers only have to get through once. And it’s about to get much, much worse,” writes Marie Korolov for Data Center Knowledge, effectively explaining the biggest roadblock for …
What’s the difference between ML and AI in Cybersecurity? Many don’t understand how AI works in terms of cybersecurity, despite most agreeing that it is necessary to implement it going forward. According to a recent survey by Webroot, nearly 75 percent of IT professionals stated they intend to incorporate more artificial intelligence (AI) solutions into their cybersecurity …
Machine Learning vs Artificial Intelligence: This Week in AI Read More →
Artificial Intelligence is Fundamental to the Future of Cybersecurity According to a 2019 SME Cyber Security Report by Senseon, eighty-one percent of small and medium-sized enterprises (SME) think Artificial Intelligence (AI) is crucial to the state and future of cybersecurity. Key findings in the report include: 88% of SMEs have a dedicated security budget 53% think an …