Behavior-Based Endpoint Protection: Advanced Threat Detection Strategies

Endpoint protection is the front line of network security. Enacting it is also getting harder as bring-your-own-device policies and rapid tech adoption leave you with more endpoints to manage than ever.
Data Security

The number of devices on your network keeps rising, but conventional defenses are quickly becoming outdated. Today’s advanced threats require advanced detection strategies. In many cases, behavior-based endpoint protection is the solution. 

What Is Behavior-Based Protection?

Behavior-based protection — also called user and entity behavior analytics (UEBA), behavioral analytics, or behavioral security — catches threats by watching for suspicious behavior. It’s similar to how you might spot a hacked social media account. The person may send you messages or post things entirely unlike them, and recognizing that behavior lets you know the account isn’t who it says it is.

UEBA applies the same concept to your business’s network. It uses machine learning to set a baseline for normal behavior to alert you when an endpoint is acting strangely. This automation is important because humans catch just 20% of insider threats on their own.

You’ll often see people use behavior-based protections to catch hacked user accounts. While that’s important, it’s equally crucial to watch for compromised endpoints, especially if your network hosts lots of devices.

EDR Solution

It’s easier to understand the importance of behavioral analysis when you compare it to more conventional methods. Traditional approaches to endpoint protection often use signature analysis. This involves identifying malicious activity by looking for known patterns or malware files.

 

Signature analysis is cheap and easy to implement, but it’s not always reliable. Using only known indicators means you can’t catch zero-day vulnerabilities — those people don’t already know about. Cybercriminals exploited 97 different zero-days in 2023 alone, so that’s a big gap. Some malware can also change its signature after you detect it, letting it slip through your defenses.

 

Cybercrime is always changing, so you have to be able to adapt. Behavior-based protections enable this adaptability by tracking normal behavior and catching anything outside those limits.

How to Implement Behavior-Based Endpoint Protection

Endpoint Protection

You can find plenty of off-the-shelf UEBA tools today, but they’re only as advantageous as your ability to use them. Here’s how you can implement these advanced detection strategies effectively.

Define Acceptable Baselines

The first step in behavior-based protection is to define acceptable endpoint behavior. Your machine learning model will typically automate this process, but it’ll require you to collect a lot of data.

 

Gather as much information as you can from event logs, authentication systems, and access permissions so these tools have enough context to set accurate baselines. Remember to encrypt and restrict access to this data, too. Just 13% of the world’s data has necessary protections, and training databases can be tempting targets to criminals if you don’t secure them.

 

It’s a good idea to tailor your behavior analysis model to your existing endpoint access permissions. Teaching the system what devices are allowed to access what data regardless of “normal” behavior will make it easier to automate policy enforcement.

Assign Unique Risk Profiles

Next, you should give each endpoint and database a risk profile. That means assigning a unique score based on how serious a breach in a given device or bit of information would be.

 

These profiles enable your machine learning model to determine which behavioral anomalies deserve the most attention. Being more careful about high-risk endpoints and data offers the most protection and helps reduce alert fatigue from false positives. Fewer unnecessary alerts mean your IT team can keep working without interruptions.

 

What defines “high risk” depends on your specific needs and business. Generally speaking, though, data with unique regulatory requirements, financial information, and devices that connect to many other systems fall under this category.

Monitor and Refine

Once you’ve established baselines and given each endpoint a risk score, you can deploy your behavior-based protection strategy. You can then inspect alerts from this system to catch and stop breaches before they cause much damage.

 

The work still isn’t over at this point — automated security tools are rarely perfect at the beginning. Consider how 43% of security professionals say 40% of their alerts are false positives. These solutions need refining over time to become more reliable.

 

Review and tweak your machine learning model at least once a year to minimize any mistakes you’ve seen it make before. You may need to adjust some baselines manually or redefine high-risk devices in this process. As you do this, the system will become more accurate, and the results will make up for any initial disruption.

Secure Your Endpoints Today

While prevention is better than a cure, you can never assume no one will ever hack into any of your endpoints. Behavior-based protection gives you the speed and accuracy you need to stop these attacks early, even if you couldn’t prevent them. These strategies are just a piece of your overall security framework, but they’re essential today.

*This article is provided by April Miller from Rehack.

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