As nearly 80% of cyber threats now emulate legitimate user actions, how are leading security operations centers (SOCs) distinguishing between authentic traffic and potential dangers?
When traditional defenses like firewalls and endpoint detection solutions fail to safeguard against crucial threats, organizations often find themselves in a tight spot. Recent findings from Verizon’s Data Breach Investigations report reveal a significant increase in breaches affecting edge devices and VPN gateways, rising from 3% to 22%. Traditional endpoint detection and response (EDR) solutions are increasingly falling short in identifying zero-day vulnerabilities and sophisticated attacks that operate without malware. Alarmingly, approximately 80% of threats now utilize stealthy techniques that are designed to closely mimic standard user behavior, as pointed out in CrowdStrike’s 2025 Global Threat Report. As threat actors continue to evolve their tactics—employing methods like credential theft and DLL hijacking—the urgency for more advanced detection measures has become clearer.
Implementing a Multi-Layered Detection Approach
To tackle these emerging challenges, SOCs are increasingly adopting a multi-layered detection framework that leverages network data to identify malicious activities that often go undetected. Technologies such as Network Detection and Response (NDR) are gaining traction, offering enhanced visibility that complements existing EDR systems by identifying patterns that endpoint-based solutions might overlook. One of the key benefits of NDR is its ability to operate without agent deployment, making it effective in uncovering threats that exploit common tools in questionable ways. Essentially, evasive tactics that succeed against edge devices and EDR are less effective when NDR is also implemented.
The Many Layers of Detection
Just as layering clothing can prepare you for unpredictable weather, elite SOCs enhance resilience through detailed threat detection strategies rooted in network insights. By consolidating these diverse detections into a unified system, NDR enables teams to manage security more efficiently and concentrate on higher-risk scenarios.
The Base Layer
The foundation of this layered approach is built on quick, easily deployable measures designed to capture known threats:
- Signature-Based Detection: This initial layer relies on established signatures—like those from Proofpoint ET Pro running on Suricata engines—to promptly identify recognized threats and attack vectors.
- Threat Intelligence: Composed of indicators of compromise (IOCs), such as specific IP addresses and domain names linked to genuine attacks, threat intelligence allows for swift and lightweight detection that can be readily shared across teams.
The Malware Layer
The next layer, focused on malware detection, acts like a protective barrier against malware payloads. Utilizing YARA rules, which are standard in the malware analysis field, this layer identifies malware families based on common code structures. It is particularly essential for spotting polymorphic malware that frequently alters its signature while retaining key behavioral traits.
The Adaptive Layer
The most advanced detection layers are integrated with behavioral insights and machine learning algorithms that can identify known, unknown, and evasive threats. These sophisticated tools include:
- Behavioral Detection: This method focuses on identifying risky activities, such as domain generation algorithms (DGAs) and unusual data exfiltration. Its effectiveness persists even as attackers change their IOCs, enabling quicker identification of new threats.
- Machine Learning Models: Utilizing both supervised and unsupervised approaches, these models can pinpoint both familiar attack patterns and anomalous behaviors that suggest fresh threats.
- Anomaly Detection: Leveraging unsupervised ML, this aspect monitors deviations from regular network behavior. These alerts help SOCs flag unexpected services and potentially malicious activities that might otherwise go undetected.
The Query Layer
Lastly, in scenarios that demand rapid alerts, search-based detection—using log search queries—serves as an immediate layer for fast responses.
Unifying Detection Layers with NDR
What truly amplifies the effectiveness of multi-layered detection is the synergy among them. Leading SOCs are implementing Network Detection and Response (NDR) technology to forge a cohesive overview of threats across their networks. By correlating data from various detection mechanisms, NDR provides a holistic picture of threats, enhancing real-time incident response capabilities.
Advanced NDR solutions present several additional advantages that bolster overall threat response efforts:
- Identification of novel attack vectors that traditional EDR solutions may miss.
- A reduction in false positives, achieving approximately 25% lower rates, according to a 2022 FireEye report.
- Enhanced incident response times through AI-driven triage and automated workflows.
- Extensive coverage of techniques outlined in the MITRE ATT&CK framework.
- Utilization of shared intelligence and community-driven detection, including open-source solutions.
The Future of SOCs
Given the rising sophistication of attacks and the expanding threat landscape, the pivot towards multi-layered detection strategies has become imperative. As threats can materialize within seconds, the opportunity to maintain robust cybersecurity measures without NDR solutions is shrinking rapidly. Top-tier SOC teams understand this necessity and have already begun integrating layered detection approaches into their operations. The pressing question is not whether organizations should adopt multi-layered detection but rather how swiftly they can implement such strategies.
Corelight Network Detection and Response
Corelight offers an integrated Open NDR Platform, combining various network detection methodologies backed by open-source software like Zeek®, tapping into community-driven detection intelligence. For further insights, visit Corelight.
