By 2021 there will be one new Zero-Day attack launched daily compared to 2015 when we only saw one per week. Zero Day attacks pose the biggest threat to companies today because they come without warning and are impossible to catch with Supervised Learning, which can only look for a threat once it has been labeled as problematic by a human.
With 37 percent of attacks in 2018 being Zero-Day as opposed to 25 percent in 2017, we need to do better. Unsupervised machine learning could help even the playing field, allowing for these attacks to be detected before they begin wreaking havoc on a network.
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 that can incorporate not only network data, but Cloud Data and SIEM logs as well.
MixMode AI uses its Unsupervised Learning to monitor deviations from the baselines of multiple streams (Cloud, Network Data and SIEM) in order to catch suspicious activity more quickly and effectively than any other security system on the market.
Supervised Learning Limitations
Other security systems currently available use Supervised Learning to monitor only network data streams because that is all that Supervised Learning is capable of doing.
Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching to find deviations in any pattern.
That’s why we are able to apply our Unsupervised Learning not only to network data but also to monitor CloudTrail Data and SIEM Alerts concurrently. That way, if an event occurs on all three platforms, the AI is much more likely to pick it up watching all three than if it were only monitoring one.
Multi-Stream Viewing to Better Detect Deviations
The way our Multi-Stream Platform works is by taking all three data streams and allowing for simultaneous viewing between them. With MixMode’s new User Interface, a security professional has a clear view of all three data streams and where alerts are popping up that match up on multiple streams.
We are able to take data from network monitoring, the cloud, and SIEM logs. The AI will flag deviations from the baseline of each system separately to see if there are any corresponding deviations at the same timestamp which may indicate an attack at that time.
MixMode is capable of taking data from Google Cloud, Amazon Web Services, Splunk, and many more data log services and scan all three at the same time, but through different channels, in order to analyze where there may be matching deviations on multiple input data streams. This way the information about a breach will be confirmed by more than one data stream reporting an abnormality on the network.
Setting Your Network Baseline
Seven days after installing MixMode, your network’s baseline is created in the system and it will begin to scan the network for deviations from this baseline constantly. This way any sort of irregularity will be caught by MixMode.
Multiple streams not only allow us to monitor more data, but aids in correlating the abnormalities across the networks.
The AI will flag the specific time of the anomaly, and show on one screen which streams were affected. This helps security professionals prioritize which threats to tackle first.
The ability to take data from multiple streams like CloudTrail, Bro, and Splunk, is an asset that no other security system on the market has yet.
Benefits of Multi-Stream Analysis
With a multi-stream platform, you have the added benefit of correlation. You can visualize what exactly the AI is showing across each stream and understand where alerts coincide over multiple streams which aids in detecting an actual hack as opposed to a false positive that says there is a problem on one stream when the others are not affected.
Another one of the benefits Multi-Stream capability provides security teams is an effective elimination of false positive security alerts. By centralizing the alerts of multi systems and streams in a single pane of glass, it is much easier to understand what is truly dangerous and what is just noise.
False positive security alerts account for hundreds of hours of labor from company employees that could be better spent hardening the network or chasing down true threats.
Adding unsupervised context-aware AI like MixMode to the mix can reduce overall alerts by 90% even further reducing the number of erroneous alerts and giving your security team more time to focus on the ones that matter.