At MixMode our one algorithm is capable of catching any anomaly that may appear on the network. In contrast, other security programs rely on a reactive method of patching and constantly adding to their algorithms each time a hack occurs so that the network learns what to look out for.
Obviously this outdated method of labeling and creating rules for each hack does not protect against future attacks that have never been seen. Cybersecurity can no longer be a learn and react model. Cybersecurity leaders have to beat bad actors to the chase knowing their tech and tools are being weaponized and re-tooled every minute of every day.
The benefit of a cybersecurity platform like MixMode that leverages Third-Wave AI (as defined by DARPA) is not only a streamlined process without need for constant patches and improvements, but a predictive ability to see what’s on the network and what should be on the network at all times.
At MixMode we use predictive AI that creates a baseline of a company’s network security profile and monitors it 24/7 to check for any anomalies that may appear on the network.
If something is caught, it is labeled and flagged as suspicious, just like other programs do. However, these second and first-wave security solutions are missing the most important aspect of a truly secure system: being able to know what the network should look like at all times in order to distinguish what is anomalous behavior and what is normal.
This gives companies who depend on Third-Wave AI technology the ability to feel safe no matter what the attack may look like.
Let’s look at a few examples of advanced 2020 hacking methods and how MixMode is able to prevent them from occurring and potentially costing enterprises millions of dollars.
Ransomware is on the tip of everyone’s tongues lately. Most recently, the Trickbot Trojan attacks that have struck almost two dozen United States hospitals and health care organizations.
Oftentimes, when we think of ransomware we immediately want to turn to an endpoint solution.
However, according to MixMode CTO Dr. Igor Mezic, “If they’ve reached the endpoint, it’s already over. A security system’s job should be to ensure they never get that far.”
What we do at MixMode with Third-Wave AI is not endpoint focused. We believe that the intruder should be caught before they have time to do any damage to files.
That’s the key reason why a Predictive AI system is so good at preventing Ransomware type attacks, because hackers will never get to the endpoint if they are discovered just as soon as they enter the network.
Once the attacker is on the endpoint the encryption can happen in seconds. It’s up to the AI to catch it before it gets to that point. When it’s a new attack that no one has ever seen before, there is no way second and first-wave AI security solutions can protect against it.
The only way to stop an attack no one has seen previously is to catch it on the network before it reaches the endpoint, and the only way to do that is to employ a Third-Wave AI system which is capable of flagging anomalies (even never before seen ones) as they arrive.
Attackers have a variety of ways of entering the endpoint, so writing a rule to try and prevent the attacker from entering is useless because there are infinite different ways to try and attack.
GANs (Generative Adversarial Networks)
GANs are some of the most advanced methods of hacking out there, and seem to have stumped many cybersecurity professionals who are looking for a secure way to protect themselves.
The way most GANs work is by creating one type of attack after another in rapid succession. It will basically test the neural network to try and infiltrate and learn what it doesn’t like in order to create something close enough to enter and allow the hackers to wreak havoc.
MixMode’s generative Third-Wave AI system is built to deflect exactly this. It will catch each anomaly as it comes and no matter what form it shapeshifts into, it will still be considered an anomaly and flagged by MixMode’s AI for disrupting the enterprise’s network.
A Man in the Middle Attack is when an attacker secretly relays and possibly alters the communications between two parties who believe that they are directly communicating with each other.
Most algorithms are not able to catch this type of attack because someone gets into the network, then redirects traffic from the victim’s IP to the machine that they have infiltrated. The victim however cannot see this because they are still seeing the network traffic behave normally themselves, it’s just been redirected as well.
The approach used by first and second-wave AI security vendors is to try and figure out if some traffic has been redirected. This is only after the intruder already got in, redirected it, and did whatever damage they wanted to do.
Having a single AI algorithm, like MixMode’s, applied to all data on the network is a proactive, predictive approach that alerts analysts before a “Man the Middle” attack even occurs. This type of attack would most certainly behave differently on a network when the AI compares it to regular daily inbound and outbound behavior.
Proactive vs. Patching
Cybersecurity has traditionally been approached reactively and threat-centric. This was OK when your sensitive data was stored in on-prem and owned data centers. But changes in digital transformation, globalization, the public cloud, and a mobile workforce are becoming the norm, data and users are spread globally and way beyond the perimeters of walled-off networks and data centers.
A reactive, patch-a-threat approach to cyber security is no longer effective. For unknown, never-before-seen, future threat detection, proactiveness is key. And that’s what third-wave AI is able to provide. Schedule a demo of MixMode today.
*** This is a Security Bloggers Network syndicated blog from MixMode authored by Ana Mezic. Read the original post at: https://mixmode.ai/blog/stop-patching-leaks-in-your-cybersecurity-boat-a-streamlined-cybersecurity-ai-solution-to-adversarial-attacks/