11 days. That’s the global median dwell time for attackers in 2024,down from 26 days when external entities notify, but still long enough to cause significant damage.
Your firewalls? They’re stopping known signatures. Endpoint tools see individual machines. But the network layer, where attackers actually move around, escalate privileges, steal sensitive data, that’s often a blind spot.
Here’s the thing about network security: most organizations are fighting yesterday’s war with tomorrow’s threats.
User and Entity Behavior Analytics (UEBA) at the network level spots the behavioral anomalies that happen before major data breaches. Those weird authentication patterns. Privilege escalation that happens slowly. Data access that’s just slightly off normal behavior patterns.
While UEBA threat detection is often discussed in the context of endpoint or cloud analytics, its role at the network layer is both underexplored and underutilized.
Sophisticated attackers don’t use malware signatures anymore. They blend in. They mimic legitimate user behavior while systematically exploring your corporate network. Traditional security methods miss this entirely.
Why Network Layer UEBA Systems Actually Work
Look, not all entity behavior analytics solutions are created equal. Some vendors slap “behavioral analytics” on basic anomaly detection and call it a day. Real network positioned UEBA systems capture comprehensive visibility that endpoint and perimeter solutions simply can’t see.
But what happens when attackers move laterally without tripping traditional security tools?
Fidelis Network® collects over 300 metadata attributes from network communications.
Basic NetFlow? Maybe a dozen data points. It’s like comparing a security camera to a flip phone camera.
Attackers don’t sleep. Neither should your defenses.
What makes user entity behavior analytics actually useful:
Complete network traffic visibility (north south AND east west. Yes, both directions matter) Protocol agnostic analysis across all network devices and ports Encrypted traffic insights without breaking encryption Historical correlation for behavioral baseline establishment going back months
Most security teams are drowning in alerts. Network UEBA systems shouldn’t add to that pile; they should provide context that helps you understand which security threats actually matter.
Real Breach Scenarios: Where UEBA Solutions Make the Difference
Before we dive into the details, let’s look at some real-world scenarios that illustrate why behavioral analytics at the network layer are so effective.
Advanced Persistent Threats: The Patient Adversary
Advanced persistent threats are patient. Painfully patient. They establish footholds, then gradually expand access over weeks or months while monitoring user behavior patterns to blend in.
Here’s how it typically unfolds: phishing email leads to initial compromise. Then reconnaissance unusual network scanning patterns that don’t quite trigger existing security systems. Credential harvesting follows authentication attempts against multiple user accounts that look almost legitimate. Finally, lateral movement communications between critical systems that normally don’t talk.
Each phase creates behavioral signatures that UEBA threat detection can identify. The timing patterns look different from normal behavior. Access sequences don’t match typical user behavior patterns. Data volumes are slightly off established behavioral baselines.
Here’s where things get interesting.
Fidelis Network®‘s Deep Session Inspection® technology analyzes application layer communications while correlating activities with established normal behavior patterns. When someone accesses financial databases at 3 AM from a marketing user account that’s not showing up in firewall logs. But it’s blazing in behavioral analytics.
In a recent financial sector breach, attackers blended into encrypted traffic patterns for weeks. Without UEBA systems correlating identity shifts and session anomalies, the intrusion would have gone unnoticed until the exfiltration phase.
Anomaly spotting
Lateral movement check
Encrypted traffic view
Role-based baselines
Insider Threat Detection: The Nightmare Scenario
In one recent investigation, network behavioral anomalies cut containment time by half because the organization could track unusual data access patterns before the insider attempted to protect sensitive data from being exfiltrated.
Detecting insider threats presents every CISO’s nightmare. They have legitimate credentials, understand your security systems, know exactly what sensitive data is valuable and where it lives.
Network-focused user behavior analytics creates individual profiles for each authorized user. When employees start accessing sensitive data outside normal parameters downloading customer lists they’ve never touched, transferring unusual volumes to external shares behavioral analytics generates risk scored alerts.
The key insight for insider threat detection? Most malicious insider activities don’t happen overnight. Someone gets disgruntled, starts exploring systems they shouldn’t, tests small data transfers, then escalates. UEBA solutions catch this progression where other security tools see nothing wrong.
That’s the red flag entity behavior analytics is built to catch.
Compromised Account Detection: More Common Than You Think
This one’s everywhere. Attackers get valid credentials through brute force attacks, phishing, or just buying them on dark web markets. Then they walk right through your defenses, accessing critical systems like authorized users.
Even with legitimate credentials, attackers behave differently than the real users they’re impersonating.
Geographic inconsistencies? The marketing manager suddenly logging in from Eastern Europe. Device fingerprints that don’t match user history? New browser configurations, different operating systems. Access patterns outside normal scope? The sales rep suddenly interested in HR databases.
Network layer user entity behavior analytics captures this because it sees the complete picture of user communications and file access patterns. Traditional security measures miss these subtle differences entirely.
Technical Implementation: What Actually Works
So, how does this all come together on a technical level? Here’s how the key machine learning approaches work in practice.
The Theory: Machine Learning that is Fit for Purpose
Too many security vendors slap “AI powered” on everything. Real UEBA systems use multiple approaches strategically, not just whatever’s trendy.
Supervised machine learning trains on known attack patterns useful for identifying variations of established security threats. Unsupervised learning finds genuinely novel patterns without requiring labeled training data. This catches zero day exploits and completely new attack methods that bypass existing security systems.
Statistical models create mathematical representations of normal user behavior patterns. These behavioral baselines adapt to legitimate changes while maintaining sensitivity to detect anomalies and detect unusual behavior.
The Practice: How This Plays Out
Honestly? The secret sauce is combining all three approaches. You get accuracy from supervised learning, discovery from unsupervised techniques, precision from statistical analysis.
Real world example: A global manufacturing company noticed unusual HTTPS traffic patterns on weekends. Traditional security tools saw encrypted traffic to legitimate cloud services nothing suspicious. But Fidelis Network®‘s behavioral analytics flagged the timing patterns, data volumes, and session characteristics as inconsistent with normal user behavior patterns. Turned out to be lateral movement disguised as legitimate cloud backup traffic.
The attackers had compromised weekend shift user accounts but couldn’t mimic the actual backup software’s behavioral fingerprint when accessing critical systems.
Data Collection: Going Beyond “We Monitor Network Traffic”
Most security solutions claim network visibility. Few deliver the depth that actually matters for user behavior analytics.
Flow Analysis Theory: Communication patterns, session durations, protocol usage across network devices. But you need behavioral context around timing, volumes, connection relationships to establish proper behavioral baselines.
How this actually works: Take a recent retail breach investigation. The attacker used legitimate remote access tools to move laterally across the corporate network. Traditional flow analysis showed normal RDP connections. But Fidelis Network® captured session timing patterns, keystroke intervals, and application usage sequences that revealed automated tool usage rather than normal user behavior.
The behavioral difference? Humans pause, make typos, navigate inconsistently. Automated tools maintain precise timing patterns that stand out in detailed security monitoring and data analytics.
Protocol Specific Analysis: HTTP behaves differently than DNS, which behaves differently than SMB. Good UEBA solutions understand these differences and spot protocol abuse, tunneling attempts, and command and control communications across all network devices.
Session Reconstruction in Action: You’re not just seeing that two systems communicated you understand what they actually did with sensitive data.
A case in point: Healthcare organization detected data exfiltration through what appeared to be normal database queries. Session reconstruction revealed SQL injection patterns hidden within legitimate application traffic. The queries followed normal behavior patterns for timing and complexity, but the data access patterns were completely wrong for the specific user’s role and usual file access patterns.
Integration: Making It Work with Your Existing Security Stack
Effective UEBA doesn’t operate in a vacuum—it gains real power when plugged into your broader security ecosystem. Here’s how integration adds value.
SIEM Enhancement: Finally, Context That Matters
Most SIEM platforms are alert factories generating countless false positives. User entity behavior analytics integration changes this by adding behavioral context to traditional event management and security event correlation.
When your SIEM sees failed login attempts, UEBA systems tell you whether those attempts fit normal user behavior patterns or suggest brute force attacks. When SIEM flags unusual file access, behavioral analytics provide the context to determine if it’s legitimate work or potential data theft from critical systems.
Result? High confidence alerts that actually require attention. Security analysts can focus on real security threats instead of chasing false positives and alert fatigue.
Endpoint Correlation: The Missing Piece
But what about when network behavioral anomalies need endpoint validation for comprehensive security monitoring?
Network layer UEBA systems provide massive value when correlated with endpoint security data. Fidelis’s NDR integration with endpoint solutions shows how this works in practice.
Real scenario: Network detected unusual authentication patterns for a finance user multiple failed attempts followed by successful login from a new device. Concerning, but not definitive. Endpoint correlation revealed that the successful login coincided with installation of unauthorized remote access software and immediate access to financial systems the specific user had never touched before.
Separately, each signal was interesting. Together, they painted a clear picture of compromised accounts and immediate threat escalation across critical systems.
Fidelis NDR complements entity behavior analytics UEBA by not only detecting deviations in user behavior but also validating these anomalies against full packet network telemetry, an anomaly detection capability many UEBA only platforms lack.
SOAR Integration: Automation That Actually Helps
Security orchestration platforms benefit from behavioral analytics by incorporating context into automated response workflows. When behavioral risk scores exceed thresholds, SOAR platforms can execute appropriate responses account isolation, network segmentation, investigation workflows.
The key is context aware automation for security teams. Not every anomaly requires the same response. User behavior analytics provide the nuanced risk assessment that enables intelligent automated responses to potential threats.
One financial services company automated their incident response using UEBA risk scores. Low risk behavioral anomalies trigger automated investigation workflows. Medium risk anomalies prompt analyst notification with pre staged evidence. High risk anomalies immediately isolate affected user accounts and initiate emergency response procedures across all security systems.
The difference is nuance. Not all behavioral anomalies indicate security threats, but the right automation can ensure appropriate responses without overwhelming security teams with false positives.
Operational Reality
Turning analytics into action isn’t always straightforward. Let’s break down how these capabilities translate into day-to-day defense.
False Positive Reduction (About Time)
Traditional network security tools are false positive factories. Every security team knows this pain. It’s exhausting and impacts business and security needs.
UEBA systems address this through contextual behavioral analysis. Instead of alerting on every unusual activity, they consider user roles, typical usage patterns, environmental factors, and historical behavioral baselines when evaluating potential security risks.
Example: Database administrator accessing customer records at midnight might trigger traditional security measures. But if that admin regularly works night shifts and has legitimate access to those critical systems, behavioral baselines account for this pattern. The alert gets contextualized rather than flagged as high priority, reducing false positives.
Investigation Acceleration
When UEBA solutions generate alerts, they come with rich contextual information. Activity timelines, risk assessments, relationship mappings, behavioral deviation analysis from established baselines.
Ignore the noise. Focus on behavior.
Security analysts can quickly understand attack progression through the corporate network, identify affected critical systems, and assess compromise scope. Instead of hunting through multiple security tools and correlating disparate data sources, everything’s integrated into coherent threat narratives.
Implementation Realities (The Hard Truth)
User entity behavior analytics isn’t plug and play. Anyone telling you otherwise is lying or selling something. Effective deployment requires 30 90 days of baseline establishment for normal behavior patterns, careful tuning, ongoing optimization across all network devices.
Network environments vary dramatically. User populations, application behaviors, communication patterns across critical systems it all affects behavioral modeling. Good UEBA systems accommodate this diversity while maintaining anomaly detection capabilities across different segments and use cases.
The payoff? Proactive threat detection that catches attacks other security systems miss entirely. But you have to do the work upfront to establish proper behavioral baselines and fine tune detection of unusual behavior patterns.
Comprehensive Threat Detection & Analysis
Data Loss Prevention (DLP) & Email Security
Deep Session Inspection & TLS Profiling
Future Trends in UEBA Network Security Technology
Looking ahead, several innovations are poised to reshape what’s possible in network-based behavioral analytics. Here’s what’s on the horizon.
Advancement in Behavioral Analytics
Advanced analytics continue advancing UEBA threat detection capabilities through improved pattern recognition, automated feature engineering, and adaptive learning algorithms that enhance detection accuracy while reducing configuration overhead for security teams.
Future implementations will incorporate graph analytics to understand complex relationships between user accounts, network devices, and data access patterns more effectively. Deep learning techniques will identify sophisticated attack patterns that current behavioral analytics systems cannot recognize through traditional statistical models.
Zero Trust Architecture Integration
Zero Trust security models align with entity behavior analytics principles through continuous verification and behavioral monitoring throughout the corporate network environment, supporting business and security needs.
User behavior analytics provide essential behavioral context for Zero Trust implementations. Dynamic access decisions based on real time risk assessments and continuous behavior evaluation across all critical systems become more granular and context aware.
Cloud Services and Hybrid Environment Support
Organizations increasingly operate hybrid and multi cloud environments requiring UEBA solutions that provide consistent behavioral monitoring across diverse infrastructure platforms and cloud services.
Modern implementations support cloud native deployments while maintaining behavioral monitoring for distributed workforces accessing sensitive data and critical systems from various locations and devices.
Frequently Ask Questions
How does entity behavior analytics differ from traditional security monitoring?
Traditional security tools identify known threats through predefined signatures and static rules. User entity behavior analytics establishes dynamic behavioral baselines and detects anomalies indicating unknown threats or sophisticated attacks that evade signature based detection through advanced evasion techniques.
Can UEBA systems effectively analyze encrypted network traffic?
Yes, through comprehensive data analytics, flow pattern analysis, and connection behavior monitoring without requiring decryption. Systems examine communication timing, data volumes, and connection characteristics to identify anomalous behavior in encrypted communications. Advanced implementations also support TLS fingerprinting and certificate analysis for additional behavioral context.
How long before behavioral analytics actually work in my environment?
Effective implementation typically requires 30 90 days of comprehensive data collection to establish accurate behavioral baselines for normal user behavior patterns, depending on network complexity and user population diversity. Account for seasonal variations, business cycles, and organizational changes when establishing baselines across critical systems.
Does user behavior analytics scale to enterprise networks with multiple security systems?
Modern UEBA solutions employ distributed processing architectures and optimized machine learning algorithms to handle enterprise scale networks without performance impact. Cloud based implementations offer elastic scaling advantages for organizations with varying network loads and growing security requirements across multiple network devices.
The post How UEBA Enhances Threat Detection Across the Network Layer appeared first on Fidelis Security.
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