Lateral movement is no longer a secondary concern—it’s a core phase of modern cyberattacks. Once attackers breach an initial endpoint, they don’t strike immediately. Instead, they pivot silently across the network, escalate privileges, and hunt for sensitive assets. The longer they dwell, the more damage they’re capable of. That’s why detecting lateral movement with behavioral analysis is essential for modern cybersecurity defense.
This blog takes a deep dive into how behavior-based threat detection, especially when paired with Fidelis XDR and NDR, can uncover even the stealthiest signs of lateral movement before they escalate.
What is Lateral Movement in Cybersecurity?
Lateral movement refers to the techniques adversaries use after an initial breach to navigate through the network, access multiple systems, and eventually reach high-value targets like domain controllers, databases, or cloud storage.
These lateral movement techniques are often low and slow—disguised as legitimate user actions, which makes traditional signature-based defenses fall short. Attackers exploit remote services, leverage stolen credentials, or inject malicious payloads across endpoints. That’s why lateral movement detection requires context—behavioral context.
Behavioral Analysis vs. Signature-Based Detection:
AspectBehavioral AnalysisSignature-Based Detection
Detection MethodMonitors patterns of behavior across users, endpoints, and networksMatches activities to predefined rules or known malware signatures Adaptability to New ThreatsLearns and evolves with your environment to detect novel or stealthy attacksRequires frequent updates; struggles with unknown or zero-day threatsDetection of Insider ThreatsIdentifies subtle behavioral deviations and compromised account activityOften misses threats that resemble legitimate user behaviorFalse Positive RateLower, due to context-aware anomaly detectionHigher, due to rigid rules and lack of behavioral contextOperational EfficiencyEnables proactive threat hunting and reduces alert fatigueReactive; can overwhelm SOCs with noise and irrelevant alertsBest Use CasesAdvanced persistent threats, lateral movement, post-exploitation behavior detectionTraditional malware detection, known phishing signatures, rule-based risks
Maturing Advanced Threat Defense
4 Must-Do’s for Advanced Threat Defense
Automating Detection and Response
Why Traditional Detection Falls Short
Firewalls, antivirus tools, and even older SIEM solutions count on pre-set rules or known indicators. But attackers have changed the game. They fake user identities, copy admin actions, and hide within regular network activity.
Behavioral analysis offers another way. It tracks patterns, learns what’s normal, and spots anything unusual using user and entity behavior analytics (UEBA). Instead of looking at just the actions, it considers how those actions happen. This opens up new ways to detect behavioral anomalies and spot threats.
The Power of Behavioral Analysis in Threat Detection
Older security tools were designed in a time when threats followed predictable patterns with clear indicators. These tools still serve a purpose but struggle to handle today’s flexible and hidden threats that often appear normal. This gap is why behavioral threat detection now plays an important role in cybersecurity.
Behavioral analysis focuses on knowing what is typical and then spotting anything unusual. Instead of just searching for malware patterns or risky IP addresses, it examines how users act how systems connect, and how information moves within the network. This kind of detection based on behavior provides a more flexible and effective way to identify advanced threats that traditional tools often miss.
Let’s break this into main parts:
Behavioral Analytics: This begins by gathering and studying a lot of data about what users and systems do. The system pays attention to patterns over time to figure out what’s normal. This helps it notice when something doesn’t fit that pattern. Behavioral Anomaly Detection: This is where the system catches unusual activity. If a user accesses a part of the system they’ve never used before, or if an app connects to a strange endpoint, these may seem like small changes but can signal a breach. These signs often uncover threats that regular signature-based tools don’t catch. Endpoint Behavior Analytics: Observing activity at the device level—like file access habits, changes to the registry, or attempts to raise privileges—offers an additional layer of insight. It helps uncover what attackers do after they compromise a device especially in finding post-exploitation behavior.
Moving away from signature-based approaches allows for more flexibility. Whether identifying lateral movement in cloud environments, or internal threats detecting these risks becomes more effective.
Detecting Lateral Movement with Behavioral Analysis: How It Works
Let’s break down the core of detecting lateral movement with behavioral analysis:
1. Behavioral Modeling of Users and Assets
2. Indicators of Lateral Movement
Behavior-based systems can flag common indicators of lateral movement, including:
Multiple failed login attempts across endpoints. RDP or PowerShell activity from unusual hosts. Privilege escalation attempts. Abnormal east-west traffic in internal networks. Access to systems not aligned with user roles.
When such behaviors align with post-exploitation behavior detection, they point toward network lateral movement in progress.
3. Correlation Across Domains
Using Extended Detection and Response (XDR), platforms like Fidelis Elevate correlate behavioral anomalies across endpoint, network, and cloud environments.
For instance:
An endpoint behavior anomaly (e.g., unusual registry modification) is linked with a network detection and response (NDR) alert about suspicious lateral traffic. This correlated view gives security teams better clarity into how to detect lateral movement in real-time.
The Fidelis Advantage: Behavioral Threat Detection in Action
Fidelis takes behavioral threat detection a step further by combining:
Deception tech to lure attackers into revealing movement. Machine learning for threat detection, enhancing accuracy over time. Deep packet inspection via NDR to uncover hidden network behaviors. Endpoint behavior analytics for precision threat tracing.
This integrated strategy allows organizations to monitor lateral network movement, even in hybrid and multi-cloud environments. Whether it’s cloud lateral movement detection or endpoint-level threat modeling, Fidelis connects all the dots.
Understand the attack lifecycle
Build a threat-informed SOC
Reduce dwell time and impact
Stopping Lateral Movement: Prevention Meets Detection
Knowing how to stop lateral movement in a network involves more than spotting it. It demands architectural defense as well:
1. Micro-Segmentation
Wondering how does micro segmentation prevent lateral movement within a network?
By isolating workloads and applying strict policies, micro-segmentation ensures that even if attackers compromise one segment, they can’t freely roam. Fidelis supports network segmentation strategies, making it harder for threats to pivot undetected.
2. Real-Time Alerts and Automation
Fidelis: Your Partner in Proactive Lateral Movement Detection
When attackers make their move across your network, will you be ready?
With Fidelis NDR and Fidelis Elevate XDR, you’re equipped to:
Spot early signs of lateral movement. Map attacker behavior across endpoints, cloud, and network. Leverage user and entity behavior analytics (UEBA) to stay ahead of threats. Automate containment and response at scale.
Fidelis doesn’t just react—it anticipates. And in the world of lateral movement detection, that’s the edge your organization needs.
Correlate endpoint and network
Stop lateral movement early
Automate deep threat response
The post Detecting Lateral Movement with Behavioral Analysis: A Fidelis Deep Dive appeared first on Fidelis Security.
No Responses