Hybrid Architecture Based on Multi-Layer Telemetry and Semantic Correlation for Advanced Threat Detection in Enterprise Environments

1. Introduction

Effective threat detection constitutes the core of modern security operations in complex corporate environments. The growth of distributed infrastructures, the intensive adoption of cloud services, and the expansion of the attack surface have substantially increased the challenge of identifying malicious behavior in a timely manner. Although preventive mechanisms such as next-generation firewalls, EDR (Endpoint Detection and Response) systems, and XDR platforms have evolved considerably, adversaries continue to exploit new vulnerabilities, abuse legitimate identities, and employ living-off-the-land techniques to bypass traditional defense mechanisms (BEHL; BEHL, 2017).

Widely documented incidents demonstrate how quickly critical vulnerabilities can be exploited. The Log4Shell vulnerability (CVE-2021-44228), for example, enabled remote code execution at scale in systems based on the Apache Log4j library and was exploited within hours of its public disclosure (CISA, 2022). Contemporary ransomware campaigns also evidence the systematic use of complex attack chains, frequently modeled according to techniques cataloged in the MITRE ATT&CK framework, including T1059 (Command and Scripting Interpreter), T1003 (Credential Dumping), and T1486 (Data Encrypted for Impact) (MITRE, 2023).

In addition, modern corporate environments include cloud-native resources, containers, and serverless platforms, which demand greater visibility and correlation of events from multiple sources. Emerging security tools, such as CNAPP platforms and observability mechanisms based on eBPF, enable the collection of detailed telemetry across different layers of the system, creating opportunities for more robust detection approaches (SCARFONE; SOULE; DODSON, 2023).

In light of this scenario, the central problem of this work can be formulated as follows: how can malicious activities distributed across multiple layers of a heterogeneous infrastructure be detected with high precision and low latency, while maintaining operational scalability and acceptable false positive rates?

We formulate three main research questions:

RQ1: How can heterogeneous telemetry from network, endpoint, identity, and cloud environments be integrated into a unified detection model?

RQ2: How can false positive rates be reduced without compromising the ability to detect sophisticated multi-stage attacks?

RQ3: What is the quantitative impact of ATT&CK-based semantic correlation on improving detection metrics?

The main contributions of this work are:

• Proposal of a formal model of semantic correlation based on TTP graphs.
• Development of a hybrid architecture combining signatures, behavioral rules, and statistical analysis.
• Comprehensive experimental evaluation using public datasets and controlled attack campaigns.

The remainder of the article is organized as follows: Section 2 presents background and related work; Section 3 describes the threat model and problem formalization; Section 4 details the proposed methodology; Section 5 describes the implementation; Section 6 presents the experimental evaluation; Section 7 discusses a case study; Section 8 analyzes the results; and Section 9 presents the conclusions.

2. Background and Related Work

Intrusion detection can be formally modeled as a classification function:

$f: X \rightarrow {0,1}$

where $X$ represents the space of observable events and the value 1 indicates potentially malicious behavior. In real corporate environments, $X$ corresponds to a set of logs, network flows, system events, and authentication records originating from multiple layers of the infrastructure.

The seminal model proposed by Denning (1987) introduced the concept of statistical behavior profiles to detect significant deviations in user or system activities. Subsequently, several studies expanded this paradigm, exploring machine learning techniques for anomaly detection in network traffic and system logs (SOMMER; PAXSON, 2010).

Garcia-Teodoro et al. (2009) classified detection systems into three main categories: signature-based, anomaly-based, and specification-based. Signature-based systems offer high precision for known attacks, but have an intrinsic limitation in detecting novel threats. In contrast, anomaly-based systems can detect unknown behavior, but often suffer from high false positive rates.

In recent years, several studies have explored deep learning techniques applied to intrusion detection, including recurrent networks and LSTM-based architectures (KIM et al., 2016). However, Sommer and Paxson (2010) argue that naïve applications of machine learning in security face significant challenges, such as scarcity of labeled data, changes in adversarial behavior, and interpretability difficulties.

More recently, graph-oriented approaches have been proposed to represent complex attack chains and enable contextual correlation between apparently independent events (HASSAN et al., 2022). In this context, frameworks such as MITRE ATT&CK provide a standardized taxonomy of adversary techniques that can be used to structure semantic detection models (MITRE, 2023).

Despite these advances, important limitations remain:

• High false positive rates in purely statistical models.
• Low semantic integration across different telemetry sources.
• Difficulty correlating events distributed across hybrid and multicloud environments.

These limitations motivate the development of the architecture proposed in this work.

3. Threat Model and Problem Formalization

We assume an adversary with capabilities typically observed in modern targeted intrusion campaigns:

1. Remote code execution on compromised systems.
2. Lateral movement within the corporate network.
3. Local or domain privilege escalation.
4. Evasion of basic logging mechanisms.
5. Abuse of legitimate credentials obtained through dumping or phishing.

We model the monitored infrastructure as a directed graph:

$G = (V,E)$

where $V$ represents the set of computing assets and identities, and $E$ represents observable interactions among these elements, such as network connections, process executions, or authentications.

An attack path can be represented as a sequence:

$P = {v_1, v_2, …, v_n}$

associated with a set of adversary techniques $\tau = {\tau_1, \tau_2, …, \tau_k}$ mapped to the ATT&CK framework.

The detection objective consists of identifying the existence of a path $P$ such that:

$$ \tau(P) \subseteq ATT&CK_{malicious} $$

while minimizing detection latency and false positive rate.

4. Proposed Methodology

The proposed architecture integrates multiple telemetry layers into a unified analysis pipeline. The system is composed of the following main components:

• Network sensors based on Suricata and Zeek.
• Endpoint telemetry collected by Sysmon and Wazuh agents.
• Kernel instrumentation based on eBPF for advanced observability.
• Collection of identity and authentication logs.
• Integration with CNAPP platforms for cloud workload events.

These data sources feed a centralized processing pipeline based on the Elastic Stack, responsible for normalizing and indexing events.

The central stage of the system consists of a semantic correlation mechanism that maps observable events to ATT&CK techniques. This mechanism dynamically builds an adversary activity graph, enabling the detection of sequences consistent with known attack chains.

Algorithm 1: TTP-based correlation

Input: set of events E, ATT&CK techniques base T
Output: set of alerts A

for each event e in E:
    identify corresponding technique t
    insert event into graph G
    update temporal relationships
    if subgraph matches attack pattern:
        generate alert

Let $n$ be the number of processed events and $m$ the number of correlation rules. The approximate complexity of the algorithm is:

$$ O(n \cdot m) $$

This approach makes it possible to correlate seemingly isolated events into coherent semantic chains.

5. Implementation

The experimental implementation was carried out using a stack widely adopted in operational security environments.

Component	Function
Suricata 7	Network intrusion detection
Zeek	Traffic behavioral analysis
Elastic Stack 8.x	Event storage and analysis
Sysmon	Detailed process telemetry
Wazuh	Integrity and log monitoring
YARA 4.x	Identification of malicious artifacts
Sigma	Standardization of detection rules
Volatility 3	Memory forensic analysis

Example of a Sigma rule used to detect suspicious access to the LSASS process:

logsource:
  product: windows
  service: sysmon
detection:
  selection:
    EventID: 10
    TargetImage: 'lsass.exe'
  condition: selection
level: high

This rule makes it possible to identify attempts to read the memory of the LSASS process, which are frequently associated with credential dumping techniques.

6. Experimental Evaluation

The experimental evaluation used a combined dataset derived from academic benchmarks and controlled experiments.

Dataset	Type	Use
UNSW-NB15	Network traffic	Training and validation
CIC-IDS2017	Traffic with labeled attacks	Testing
Red Team campaigns	Corporate simulations	Operational evaluation

The results obtained are presented below.

Method	Precision	Recall	F1
Signatures	0.71	0.64	0.67
Anomaly	0.76	0.72	0.74
Proposed architecture	0.89	0.83	0.86

A 38.2% reduction in the false positive rate was observed compared to systems based exclusively on rules.

In addition, the Mean Time To Detect (MTTD) was reduced, as shown below.

Approach	Average MTTD
Baseline	6.1 minutes
Proposed architecture	4.2 minutes

7. Case Study

A controlled simulation of a ransomware attack was conducted in a virtualized corporate environment. The scenario followed a typical attack chain observed in real incidents.

Attack stages:

1. Initial compromise via phishing.
2. Execution of a PowerShell script (T1059).
3. Credential dumping through access to LSASS (T1003).
4. Lateral movement via SMB.
5. Deployment and execution of ransomware (T1486).

The proposed architecture detected the malicious activity as early as the second stage of the attack chain, associating PowerShell execution events with suspicious behavioral patterns and generating an alert with an estimated probability of 0.92.

8. Discussion

The results indicate that TTP graph-based semantic correlation significantly reduces operational noise in SOCs. Integrating telemetry across multiple layers makes it possible to identify distributed attack chains that would be difficult to detect through isolated mechanisms.

Another relevant aspect concerns interpretability. By associating events with specific ATT&CK techniques, the system produces more contextualized alerts, facilitating analysis by security analysts.

However, some limitations remain. The effectiveness of the system depends on the quality and completeness of the collected telemetry. In addition, adversary techniques that use advanced encryption, such as communications encapsulated in TLS 1.3 or covert channels, may reduce network-level visibility.

9. Conclusion

This work presented a hybrid threat detection architecture based on multi-layer telemetry and TTP-oriented semantic correlation. The proposed formal model uses dynamic event graphs to identify distributed attack chains in complex corporate environments.

The experimental evaluation demonstrated significant improvements in precision, recall, and mean detection time when compared to isolated approaches. These results suggest that integrating multiple telemetry sources, combined with semantic modeling of adversary techniques, can substantially increase the operational effectiveness of security operations centers.

Current research directions include the use of graph learning for alert prioritization, deeper integration with CNAPP platforms, and expanded observability through eBPF-based instrumentation.

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