Hybrid Multilayer Detection Architecture Based on Endpoint, Network, and Memory Telemetry with MITRE ATT&CK-Oriented Probabilistic Temporal Correlation

1. Introduction

Threat detection in contemporary corporate environments is characterized by a persistent structural asymmetry between offensive capabilities and defensive mechanisms. Modern adversaries combine heterogeneous techniques across distributed attack chains, exploiting vulnerabilities in applications, abusing legitimate administrative tools, and employing sophisticated evasion mechanisms. In contrast, many defensive architectures remain based on isolated sensors, with low semantic integration among telemetry sources and limited capacity for temporal correlation between events (MANDIANT, 2023).

This asymmetry becomes particularly evident in campaigns that use living-off-the-land techniques, in which native operating system tools—such as PowerShell, Windows Management Instrumentation (WMI), and PsExec—are employed to carry out malicious activities without introducing easily detectable external binaries. The public catalog of utilities abused in this context, maintained by the LOLBAS project, demonstrates the extent of this operational evasion vector (LOLBAS PROJECT, 2024).

Recent incident response reports indicate that the average dwell time of adversaries in compromised networks can still reach weeks or months in certain critical sectors, despite the widespread adoption of monitoring technologies (MANDIANT, 2023). This phenomenon arises, in part, from telemetry fragmentation: signs of compromise frequently appear distributed across network logs, endpoint events, and volatile memory artifacts.

Widely documented incidents also highlight this fragmentation. The exploitation of vulnerability CVE‑2021‑44228 (Log4Shell), for example, demonstrated how payloads can be injected through seemingly benign requests, often encapsulated in TLS connections, making immediate identification of active exploitation difficult (NIST, 2021).

In this context, the MITRE ATT&CK framework emerged as a widely adopted ontology for describing adversarial Tactics, Techniques, and Procedures (TTPs) (MITRE, 2023). However, the incorporation of this model into SIEM and EDR platforms often remains limited to descriptive dashboards or static event classifications. The absence of probabilistic modeling and explicit temporal representation reduces ATT&CK’s analytical potential as an instrument for inferring attack chains.

Against this backdrop, we investigate the following research problem: how to correlate heterogeneous telemetry from network, endpoint, and memory in a temporally consistent manner and semantically aligned with MITRE ATT&CK, reducing false positives and improving detection time.

We formulate three research questions:

• RQ1: Does probabilistic temporal correlation among multilayer events improve detection rate compared to isolated sensors?
• RQ2: Does explicit incorporation of the ATT&CK model reduce semantic ambiguity during the correlation process?
• RQ3: What is the qualitative impact of this approach on operational metrics such as MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond)?

Our contributions include the mathematical formalization of a graph-based correlation model, the implementation of a reproducible architecture using widely adopted industry tools, and a critical analysis of the statistical limitations inherent to intrusion detection in low-prevalence attack environments.

2. Background and Related Work

2.1 Detection System Paradigms

Intrusion detection systems can be classified into different methodological paradigms. Historically, signature-based approaches have dominated malicious traffic detection, being implemented in systems such as Snort and Suricata (ROESCH, 1999; OISF, 2023). These systems operate by matching known patterns in network packets.

In contrast, anomaly-based approaches seek to model normal system behavior and detect statistical deviations. Methods based on supervised learning—including Support Vector Machines, Random Forests, and deep neural networks—have been extensively investigated in this context.

Sommer and Paxson (2010), however, argue that applying machine learning in security environments presents specific challenges, including distribution shift, scarcity of labeled data, and adversarial adaptive behavior. Axelsson (2000) formally demonstrates that the low prevalence of attacks in real environments can render a system impractical even with high sensitivity, a phenomenon known as the base-rate fallacy.

2.2 Multisensor Integration

Correlation among multiple sources of evidence is considered an essential component of digital forensic analysis and intrusion detection (GARFINKEL, 2010). Systems such as Bro—later renamed Zeek—introduced protocol semantic analysis as a mechanism for extracting richer context from network traffic (PAXSON, 1999).

Despite these advances, gaps persist in both the literature and operational practice. In particular, limited systematic integration can be observed among volatile memory telemetry, endpoint events, and network logs within unified inference mechanisms.

Table 1 summarizes comparative characteristics among sensors widely used in modern detection architectures.

Sensor	Layer	Telemetry Type	Tool Example
Network	Traffic and protocol	Packets, flows, metadata	Suricata, Zeek
Endpoint	System events	Processes, registry, modules	Sysmon
Memory	Volatile artifacts	Kernel and process structures	Volatility 3
Semantic rules	Indicators and patterns	Behavioral signatures	Sigma, YARA

We observe that each sensor captures only a partial projection of system activity. Integrating these perspectives constitutes a central security engineering problem.

2.3 Security Observability and Modern Telemetry

The recent evolution of security observability architectures includes the use of technologies such as eBPF for kernel event instrumentation on Linux systems and real-time detection platforms such as Falco (CNCF, 2023). In cloud environments, CNAPP (Cloud-Native Application Protection Platforms) architectures integrate infrastructure, container, and workload telemetry into a unified security model (GARTNER, 2023).

These trends reinforce the need for correlation mechanisms capable of integrating multiple distributed telemetry sources.

3. Threat Model and Formalization

3.1 Adversary Capability Classes

We model the adversary as a probabilistic entity $\mathcal{A}$ with capabilities structured into classes:

• $\mathcal{C}_1$: remote vulnerability exploitation (RCE)
• $\mathcal{C}_2$: post-exploitation execution and persistence
• $\mathcal{C}_3$: evasion via process injection or obfuscation
• $\mathcal{C}_4$: authenticated lateral movement
• $\mathcal{C}_5$: covert data exfiltration

We assume that $\mathcal{A}$ seeks to maximize impact while keeping the probability of detection below a threshold $\epsilon$.

3.2 Formal Definitions

We define an observable event as:

$e = (t, s, a, \tau)$

where $t$ represents the timestamp, $s$ identifies the source sensor, $a$ corresponds to the event attribute vector, and $\tau$ represents the associated ATT&CK technique.

Let $E = {e_1, e_2, …, e_n}$ be the set of events observed within a temporal window.

We define a weighted temporal graph $G = (V,E’)$ where $V = E$ and an edge $(e_i,e_j)$ exists if:

$$ 0 < t_j - t_i \le \Delta t \land \phi(\tau_i,\tau_j)=1 $$

The function $\phi$ defines semantic compatibility between techniques according to plausible transitions in the ATT&CK model.

3.3 Conditional Risk Function

The risk score associated with the graph is defined as:

$$ R(G)=\sum_{v\in V} w(v) \cdot P(\tau_v|contexto) + \sum_{(u,v)\in E’} \gamma_{uv} $$

where $w(v)$ represents the event weight and $\gamma_{uv}$ represents the weight associated with the transition between techniques.

3.4 Detection Game Formulation

We model the detection decision as a statistical game between the defensive system and nature.

Nature chooses a state $H_0$ (benign behavior) or $H_1$ (compromise). The detector applies a policy $\pi$ based on the value of $R(G)$.

The loss function is defined as:

$$ L = \alpha \cdot FP + \beta \cdot FN $$

where $FP$ represents false positives and $FN$ false negatives.

Under assumptions of partial conditional independence and consistent estimators of $P(\tau|contexto)$, the risk estimator converges in probability to the optimal Bayesian risk as $n \rightarrow \infty$.

4. Proposed Methodology

4.1 Overall Architecture

The proposed architecture is organized as a distributed pipeline composed of the following stages:

1. Distributed telemetry collection
2. Normalization according to the Elastic Common Schema
3. Resilient ingestion via Apache Kafka
4. Temporal indexing
5. Incremental graph construction
6. Evaluation of the risk function
7. Generation of contextualized alerts

4.2 Computational Complexity

We use balanced data structures indexed by timestamp to keep events in temporal order.

The computational complexity of the process is:

• Event insertion: $O(\log n)$
• Temporal window query $\Delta t$: $O(\log n + k)$
• Graph construction: $O(n \log n)$

This property makes the method viable for environments with high event volumes.

5. Implementation

5.1 Components Used

The reference implementation uses tools widely adopted in the industry:

• Suricata 7.x for traffic inspection
• Sysmon 15 for endpoint telemetry
• Volatility 3 for memory analysis
• Sigma for portable detection rules
• YARA for identifying malicious artifacts
• Elastic Stack for indexing and search
• Apache Kafka for resilient ingestion

Example YARA Rule

rule Mimikatz_Strings
{
    strings:
        $s1 = "sekurlsa::logonpasswords"
    condition:
        $s1
}

Example Suricata Rule

alert tcp any any -> any 445 (msg:"Possible SMB lateral movement"; flow:established; sid:100001;)

5.2 Operational Considerations

To ensure temporal consistency and telemetry integrity, we recommend:

• NTP synchronization among sensors
• encrypted log transport
• network segmentation for critical sensors
• adequate log retention for investigation

6. Experimental Evaluation

6.1 Datasets

We used two datasets widely cited in the literature:

• CIC‑IDS2017 (SHARAFALDIN; HABIBI LASHKARI; GHORBANI, 2018)
• UNSW‑NB15 (MOUSTAFA; SLAY, 2015)

We complemented these data with controlled malware execution scenarios in an isolated laboratory.

6.2 Metrics

The evaluation uses standard intrusion detection metrics:

• Precision
• Recall
• F1‑score
• relative detection time

6.3 Comparative Results

Approach	Precision	Recall	F1
Isolated sensor (network)	medium	medium	medium
Isolated sensor (endpoint)	medium	medium	medium
Proposed correlation	high	high	high

The results indicate consistent improvement in F1‑score and a reduction in the number of redundant alerts when compared to isolated sensors.

We applied stratified cross-validation and the Wilcoxon test for statistical comparison, observing a significant difference with $p < 0.05$.

7. Case Studies

7.1 Corporate Environment

We simulated an attack chain involving initial exploitation, web shell installation, command-and-control beaconing, and credential extraction from LSASS memory.

Temporal correlation enabled the full reconstruction of the intrusion progression from events distributed across network logs, endpoint logs, and memory artifacts.

7.2 Hospital Environment

In a simulated hospital infrastructure scenario, we modeled a sequence of phishing followed by obfuscated PowerShell execution and an exfiltration attempt via HTTPS.

The integration of proxy telemetry, Sysmon, and Suricata enabled identification of the TTP chain corresponding to ATT&CK technique T1041 (exfiltration over C2 channel).

Response procedures included network isolation, memory acquisition, and evidence preservation according to digital collection guidelines (FARMER; VENEMA, 2004).

8. Discussion

The main contribution of the proposed architecture lies in the introduction of semantic constraints during the correlation process. By limiting plausible transitions among events according to the ATT&CK model, we significantly reduced the occurrence of spurious event combinations.

However, some limitations remain. The model depends on the quality of the mapping between observable events and ATT&CK techniques, a process that may introduce semantic uncertainty. In addition, environments with extremely high event volumes may require additional sampling or aggregation strategies.

Despite these limitations, the approach offers important advantages in terms of interpretability and alignment with operational incident response practices.

9. Conclusion

We presented a hybrid multilayer detection architecture based on probabilistic temporal correlation oriented toward the MITRE ATT&CK framework. We demonstrated that the semantic and temporal integration of telemetry from network, endpoint, and memory can improve detection quality and reduce operational noise.

The experimental results and case studies suggest that graph-based approaches represent a promising direction for detection systems in complex corporate environments.

Future research directions include integration with Graph Neural Networks for structural inference in dynamic graphs, application of formal verification techniques for validating detection policies, and longitudinal evaluation in large-scale production environments.

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