Ever thought about what to do to prevent deadly insider attacks? Even with the implementation of intrusion prevention systems and antivirus software, these threats persist. And their cost has risen by 44% over the past two years.
In 2023, insiders have been responsible for the unauthorized leakage of almost 1 billion records. Amid this adversity, user and entity behavior analytics (UEBA) has emerged as a modern enterprise security solution. It constantly flags irregular patterns that signal an impending breach.
To understand how UEBA works, we'll dive deeper and see if it's effective for organizations.
Why are current cybersecurity approaches not enough?
74% of organizations have faced security risks from within their own network (aka insider threats). It’s because they rely on signature-based detection as their primary threat detection method. Here's how it works: Intrusion prevention systems and antivirus software match attacking fingerprints to known signatures. The lack? Attackers can sidestep these security measures using methods such as:
- Denial of Service
- Application seizing
While some IPS/IDS (intrusion detection systems) counter this by comparing current traffic with baseline traffic. But, they have their drawbacks. Although they offer more customizable intrusion detection, they’re expensive and require more resources.
So, even with these measures, the existing IPS/IDS systems alone struggle to detect all attacks. This leads to false positive alerts and a lack of significant return on investment (ROI).
Defining user and entity behavior analytics (UEBA)
UEBA is a critical risk management solution that leverages ML algorithms and behavior analytics to provide comprehensive user and entity insights. By delving into these insights, security teams can identify anomalies and take measures to mitigate potential impacts.
Distinguishing itself from user behavior analytics (UBA), UEBA encompasses a broader scope. While UBA focuses solely on analyzing user behavior, UEBA covers both the behavior of users and entities within the network including:
- network devices
Take an example: Suppose a company's network registers 1,000 data requests per hour, but one day, it experiences a surge to 10,000 requests in an hour. Here, UEBA would recognize this irregularity and promptly notify the administrators.
UEBA vs. other security approaches
When comparing other security approaches, two primary methods are:
- signature-based detection.
- anomaly-based threat detection.
And here's how they work:
Signature-based detection applies predetermined rules to pinpoint known threats and trigger alerts based on recognized signatures within the monitored environment.
Since malicious activities differ from regular patterns, anomaly-based detection builds models of typical behavior to identify these deviations. And despite its capability to detect unknown threats, this approach is plagued by high false rates.
Displacing these two options, UEBA shines as a real-time detection technology for security-related anomalies and threats. It visually represents behavior patterns and ranks risks accordingly. With this, network administrators can identify abnormal behavior and take prompt action based on results.
How UEBA works
Here’s a breakdown of UEBA’s functionality—each step contributing to a comprehensive approach to security.
1) Ingesting data
The initial phase involves systematic monitoring and data collection from sources such as firewall logs, web proxy data, DNS records, and VPN activities. This data is ingested into the UEBA system for further analysis.
2) Correlating elements
The collected data is then integrated to identify relationships between different elements within the system. It helps establish connections and patterns between user behavior, host activities, and device operations.
Advanced machine learning algorithms sift through the integrated data to identify anomalies, patterns, and potential threats.
The ongoing system monitoring process involves adapting and learning from previous instances, integrating feedback provided by skilled analysts. This continuous scoring mechanism enhances the system's ability to respond to emerging threats swiftly and effectively.
5) Act accordingly
UEBA system generates a 360 view of the entity—providing a perspective on compromised users, hosts, and devices. This broad view identifies malicious insiders and facilitates partner network monitoring. Using this, security teams prioritize alerts and take action accordingly to strengthen the security posture.
Why should you use UEBA for enterprise security?
Now that you know how UEBA works, you might wonder why you should adopt this technology. So here’s why:
Decrease mean time to respond
UEBA provides the security team ample time to respond effectively against insider threats. By swiftly identifying high-risk behaviors and reducing the time required for attack responses, it mitigates potential damages before they escalate.
Minimize security risks
By identifying anomalies early, user and entity behavior analytics safeguards the data and maintains the integrity of the organization’s security infrastructure. It also protects from data losses and mitigates the potential damage caused by previous threats.
Automate threat detection
Relying solely on human expertise is a limitation to growth, especially in the AI era. But UEBA bridges this gap by leveraging machine learning and behavioral analytics. Doing so empowers organizations to compensate for any lack of cybersecurity expertise among analysts.
Reduce false-positive alerts
False-positive alerts divert attention from focusing on real security breaches. But, with UEBA, administrators can focus and work on real cases of abnormal activity. By filtering out false alarms, it enhances the operational efficiency of security teams—allowing them to address what's needed.
Now, suppose you start using this approach for enterprise security. The end result? You will have a strong overall security posture.
Measuring the effectiveness of user and entity behavior analytics
In 2021, Xi'an University of Architecture & Technology students experimented with the UEBA system—managing 530 events over the designated test period.
By applying predefined configuration criteria, the system detected anomalies related to the peer use case and then compared these anomalies with each user's historical profile. Among the total events, 103 triggered alerts to the system administrator, each associated with a specific confidence score.
A detailed analysis of the alerts received by the local administrator indicated that only one alert out of 78 events was false. This shows a false positive rate of 2.13%, a reasonably low rate compared to other systems like SIEM.
Understanding where UEBA lacks
Although the UEBA experiment demonstrated highly commendable detection rates and confidence scores, there are still the following issues:
- Changing the rules in the system is tricky and needs a lot of time.
- Despite the technology, human experts are required to check the accuracy of alerts and make decisions.
- Integrating with cloud environments restricts the system's adaptability to diverse data sources. This leads to gaps in threat detection and an incomplete overview of potential security risks.
- Some users might want their privacy to be protected, so it creates a barrier to monitor their behavior.
Secure your systems with user and entity behavior analytics
While traditional security measures often fall short due to advanced attacking techniques, user and entity behavior analytics can be your promising solution. Delving into the behavioral nuances of users and entities within an organization's network alerts you of abnormal activities in advance. This way, you can keep your systems secure by taking prompt action against potential threats.
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