Multilayer Hybrid Architecture for Proactive Detection of Advanced Threats Based on Semantic Correlation, Multisource Telemetry, and Adaptive Statistical Models

1. Introduction

Threat detection constitutes the operational core of any contemporary cybersecurity program. Although preventive mechanisms — such as system hardening, network segmentation, vulnerability management, and continuous patch deployment — significantly reduce the attack surface, empirical experience demonstrates that system compromise must be regarded as a plausible scenario within complex architectures. Widely cited reports, such as the Verizon Data Breach Investigations Report, indicate that a substantial portion of incidents involves human factors, including social engineering and credential compromise (VERIZON, 2023).

High-impact attacks observed over the last decade, such as the SolarWinds supply chain compromise and the large-scale exploitation of the Log4Shell vulnerability ($\text{CVE-2021-44228}$), show that sophisticated intruders may remain undetected for long periods while carrying out progressive reconnaissance, persistence, and lateral movement activities (CISA, 2021). Such activities frequently correspond to techniques formalized in the MITRE ATT&CK matrix, including command execution (T1059), credential dumping (T1003), and remote services for lateral movement (T1021) (STROM et al., 2018).

Modern corporate environments, characterized by hybrid infrastructures that integrate traditional datacenters, cloud services, and containerized workloads, generate massive volumes of operational telemetry. Monitoring systems can produce tens of thousands of events per second, originating from network sensors, endpoint agents, identity systems, and observability platforms. This volume makes manual analysis unfeasible, requiring automated mechanisms for event correlation and prioritization.

Formally, the problem can be modeled as the identification of malicious patterns in massive streams of heterogeneous data under latency constraints and the presence of noise. Traditional signature-based systems, such as network IDSs, are effective at detecting known threats but exhibit limitations in the face of polymorphic attacks, fileless malware, and living-off-the-land techniques that exploit legitimate operating system tools.

In light of this scenario, we formulate the following research questions:

• How can signature-based, behavior-based, and statistical anomaly detection be integrated into an operationally viable architecture?
• Is it possible to reduce mean time to detection (MTTD) without significantly increasing the false positive rate?
• How can the correlation between temporally distributed events be formally modeled while aligning them with the MITRE ATT&CK ontology?

The main contributions of this work are:

• Proposal of a hybrid multilayer architecture that integrates graph-based ATT&CK semantic correlation and adaptive statistical models.
• Development of a technique-oriented temporal correlation algorithm capable of identifying multi-stage attack chains.
• Experimental evaluation based on realistic attack scenarios using tools widely documented in the offensive security literature.

2. Background and Related Work

Intrusion detection was formally introduced by Denning (1987), who proposed a model based on statistical behavior profiles. This model underlies much of the subsequent research in anomaly detection.

Later, systems such as Snort popularized the signature-based approach, enabling the efficient identification of known attack patterns (ROESCH, 1999). However, such systems depend heavily on prior availability of indicators of compromise.

Mathematically, let us consider an event stream:

$$ E = {e_1, e_2, …, e_n} $$

Each event $e_i$ is represented by a feature vector:

$$ x_i \in \mathbb{R}^d $$

The detection objective consists of learning a decision function:

$$ f(x_i) = \begin{cases} 1, & \text{if } x_i \text{ represents malicious activity} \ 0, & \text{otherwise} \end{cases} $$

Contemporary approaches can be classified into three main categories.

Approach	Main characteristic	Limitations
Signature	Matching against known patterns	Inability to detect zero-day
Anomaly	Identification of behavioral deviations	High false positive rate
Semantic correlation	Modeling relationships between events	Dependence on ontologies and mappings

Recent literature emphasizes the use of operational ontologies, particularly MITRE ATT&CK, to model adversary behavior in terms of observable techniques and tactics (STROM et al., 2018). This approach enables detection mechanisms to identify behavioral patterns even in the absence of specific indicators.

In parallel, research in system security has explored dependency graphs among processes, files, and network connections to detect attack chains (HASAN et al., 2019). In the domain of machine learning applied to security, anomaly detection based on isolation trees has demonstrated effectiveness in high-dimensional scenarios (LIU; TING; ZHOU, 2008).

Despite these advances, relevant gaps persist in the literature. Many experimental evaluations use obsolete or synthetic datasets, and few approaches explicitly integrate operational metrics used by security operations centers, such as mean time to detection (MTTD) and mean time to response (MTTR).

3. Threat Model and Formalization

We assume an adversary $A$ capable of initial compromise through targeted phishing or exploitation of known vulnerabilities. After initial access, the adversary may perform lateral movement using common enterprise protocols, including SMB, WMI, or RDP.

We also assume that the adversary may use living-off-the-land techniques based on native operating system utilities, such as PowerShell, certutil, or rundll32, in order to reduce the probability of detection.

An attack chain can be represented as:

$$ C = (t_1, t_2, …, t_k) $$

where each $t_i$ represents a technique in the MITRE ATT&CK matrix.

The detection problem consists of identifying subsets $C’$ such that:

$$ P(C’ \mid E) > \theta $$

where $\theta$ represents an operationally defined decision threshold.

The system performance requirements are defined by the following constraints:

Metric	Requirement
Detection rate (TPR)	$\geq 0.9$
False positive rate (FPR)	$\leq 0.05$
Detection latency	$< 60s$

4. Proposed Methodology

The proposed architecture is organized into three functional layers.

Layer 1 — Collection and Normalization

Telemetry collection integrates multiple sources:

• Suricata with EVE JSON output for network events
• Sysmon for process and connection telemetry
• Active Directory authentication logs
• Linux system telemetry via auditd or eBPF

Normalization is performed by ingestion pipelines that convert heterogeneous events into a common schema indexed in the search engine.

Layer 2 — Semantic Correlation

We construct a directed graph:

$$ G = (V, E) $$

where $V$ represents ATT&CK techniques and $E$ represents transitions observed between techniques during adversary activity.

Temporal correlation algorithm:

Input: event stream E, ATT&CK graph G, temporal window Δt
Output: set of alerts A

for each event e_i in E:
    map event to technique t
    insert t into window Δt
    verify plausible paths in G
    calculate sequence score S
    if S > θ:
        generate alert

The approximate computational complexity is:

$$ O(n \cdot k) $$

where $k$ corresponds to the average number of correlated techniques.

Layer 3 — Adaptive Statistical Model

We apply the Isolation Forest algorithm to identify anomalous behavior.

The anomaly score is defined as:

$$ score(x) = 2^{-\frac{E(h(x))}{c(n)}} $$

where $h(x)$ represents the average isolation depth in random trees and $c(n)$ is a normalization factor.

5. Implementation

The experimental implementation used the following technology stack:

Component	Function
Elastic Stack 8.x	Log indexing and analysis
Suricata 7	Network IDS
Sysmon	Detailed endpoint telemetry
Wazuh	SIEM and additional correlation
MISP	Threat intelligence
Volatility 3	Memory 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: 'C:\Windows\System32\lsass.exe'
  condition: selection
level: high

The ingestion cluster consisted of three Elasticsearch nodes with 32 GB of RAM each, achieving an average ingestion rate of approximately 18,000 events per second.

6. Experimental Evaluation

The evaluation was conducted in a controlled environment containing multiple virtual machines.

Component	Quantity
Windows 10 workstations	20
Linux servers	5
Domain controllers	2

Forty-two attack scenarios were executed using tools widely documented in the offensive security literature.

Observed results:

ATT&CK technique	TPR (%)	FPR (%)	MTTD (s)
T1003	96.3	2.1	18
T1021	94.7	3.0	25
T1059	92.8	2.8	14

Comparison with a baseline based only on network IDS:

System	Mean TPR	FPR	MTTD
Isolated IDS	78.4%	1.9%	39 s
Proposed architecture	94.6%	2.6%	24 s

We observed an approximate 37% reduction in mean detection time and a significant increase in ATT&CK technique coverage.

7. Case Study

We simulated an initial phishing-based attack with remote PowerShell execution.

Observed sequence:

1. Initial PowerShell execution (Sysmon EventID 1 telemetry).
2. Payload download using certutil (T1105).
3. LSASS credential extraction via Mimikatz (T1003).
4. Lateral movement via PsExec (T1021).

The correlation mechanism identified the complete attack chain in approximately 31 seconds, generating a critical-severity alert before the data exfiltration stage.

The analysis showed that multi-stage correlation significantly reduces operational noise compared to isolated alerts.

8. Discussion

The experimental results indicate that hybrid architectures exhibit superior performance when compared to isolated detection mechanisms. Integrating telemetry from multiple sources makes it possible to observe behaviors that would not be detectable by individual sensors.

However, some limitations remain. The system’s accuracy depends on the quality of the mapping between observable events and ATT&CK techniques. In addition, changes in the normal behavior of the infrastructure may generate concept drift phenomena, requiring periodic recalibration of the models.

For SOC operations in corporate environments, we recommend adopting operational metrics based on MTTD, ATT&CK technique coverage, and periodic purple-team tests for continuous validation of detection capabilities.

9. Conclusion

We presented a hybrid multilayer architecture for proactive detection of advanced threats based on semantic correlation, temporal analysis, and adaptive statistical models. The approach integrates network, endpoint, and identity telemetry, enabling identification of multi-stage attack chains aligned with the MITRE ATT&CK ontology.

The experimental evaluation demonstrated significant improvement in technique coverage and reduction in mean detection time when compared to traditional signature-only approaches.

These results indicate that the combination of semantic correlation and statistical modeling constitutes an effective strategy for modern cybersecurity operations in hybrid and cloud-native environments.

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