A Multi-Layer Detection Hybrid Architecture Using MITRE ATT&CK-Oriented Probabilistic Temporal Correlation Based on Endpoint, Network, and Memory Telemetry

1. Introduction

Threat detection in modern enterprise environments is characterized by a persistent structural imbalance between offensive capability and defensive mechanisms. Contemporary adversaries combine heterogeneous techniques across distributed attack chains, exploiting application vulnerabilities, legitimate administrative tools, and advanced evasion mechanisms. In contrast, many defensive architectures still rely on isolated sensors, with low semantic integration across telemetry sources and limited ability to correlate events temporally (MANDIANT, 2023).

This asymmetry is particularly pronounced in campaigns that employ living-off-the-land techniques. In such cases, operating-system-native tools such as PowerShell, Windows Management Instrumentation (WMI), and PsExec are used to carry out malicious activity without introducing easily detectable external binaries. The LOLBAS project, which maintains a public catalog of utilities abused in this context, illustrates the breadth of this operational evasion vector (LOLBAS PROJECT, 2024).

Recent incident response reports indicate that, despite the widespread deployment of monitoring technologies, attackers’ average dwell time inside compromised networks can still reach weeks or months in certain critical sectors (MANDIANT, 2023). This phenomenon is partly attributable to telemetry fragmentation. Indicators of compromise often appear dispersed across network logs, endpoint events, and volatile memory artifacts.

Well-documented incidents also corroborate this fragmentation. For example, the exploitation of CVE‑2021‑44228 (Log4Shell) showed that payloads could be injected through seemingly innocuous requests, often encapsulated within TLS connections, making immediate identification of ongoing exploitation difficult (NIST, 2021).

Within this context, the MITRE ATT&CK framework emerged as a widely adopted ontology for describing adversary tactics, techniques, and procedures (TTPs) (MITRE, 2023). However, its incorporation into SIEM and EDR platforms often remains limited to descriptive dashboards or static event classification. The absence of probabilistic modeling and explicit temporal representation reduces ATT&CK’s analytical potential as a means of reasoning about attack chains.

Given this situation, we examine the following research question: how can heterogeneous telemetry from network, endpoint, and memory sources be correlated in a temporally consistent and semantically aligned manner with MITRE ATT&CK in order to reduce false positives and improve detection time?

We formulated the following three research questions:

• RQ1: Does probabilistic temporal correlation across multi-layer events improve detection rates compared with isolated sensors?
• RQ2: Does the explicit incorporation of the ATT&CK model reduce semantic ambiguity during correlation processing?
• RQ3: What qualitative impact does this approach have on operational metrics such as MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond)?

Our contributions include a mathematical formulation of a time-graph-based correlation model, an implementation of a reproducible architecture using widely adopted industry tools, and a critical analysis of the statistical limits of intrusion detection in low-frequency attack environments.

2. Background and Related Work

2.1 Paradigms of Detection Systems

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

By contrast, anomaly-based approaches attempt to model normal system behavior and detect statistical deviations. Supervised learning-based approaches, including Support Vector Machines, Random Forests, and deep neural networks, have been widely studied in this context.

However, Sommer and Paxson (2010) argue that applying machine learning to security presents distinct challenges, including distribution shift, the scarcity of labeled data, and adaptive attacker behavior. Axelsson (2000) formally showed that the low frequency of attacks in real environments can render highly sensitive systems impractical, a phenomenon known as the base-rate fallacy.

2.2 Multi-Sensor Integration

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

Despite these advances, gaps remain in the literature and in operational practice. In particular, the systematic integration of volatile memory telemetry, endpoint events, and network logs into a unified reasoning mechanism remains limited.

Table 1 summarizes the comparative characteristics of sensors commonly used in modern detection architectures.

Sensor	Layer	Telemetry Type	Example Tools
Network	Traffic and protocols	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

It can be seen that each sensor captures only a partial projection of system activity. Integrating these perspectives is a central challenge in security engineering.

2.3 Security Observability and Modern Telemetry

Recent advances in security observability architectures include the use of eBPF techniques for kernel event instrumentation in Linux systems and real-time detection platforms such as Falco (CNCF, 2023). In cloud environments, CNAPP (Cloud‑Native Application Protection Platforms) architectures incorporate infrastructure, container, and workload telemetry into unified security models (GARTNER, 2023).

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

3. Threat Model and Formulation

3.1 Classes of Adversary Capability

We model the adversary as a stochastic entity $\mathcal{A}$ with class-structured capabilities.

• $\mathcal{C}_1$: 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
• $\mathcal{C}_5$: Low-profile data exfiltration

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

3.2 Formal Definitions

We define an observed event as follows:

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

where $t$ is the timestamp, $s$ is the source sensor, $a$ is the event attribute vector, and $\tau$ represents the corresponding ATT&CK technique.

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

We define a weighted time graph $G = (V,E’)$ with $V = E$, and we say that an edge $(e_i,e_j)$ exists if the following holds:

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

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

3.3 Conditional Risk Function

The risk score associated with the graph is defined as follows:

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

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

3.4 Formulation as a Detection Game

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

Nature selects state $H_0$ (normal behavior) or $H_1$ (compromise). The detector applies a policy $\p## 4. Proposed Method

4.1 Overall Architecture

The proposed architecture is structured as a distributed pipeline consisting of the following stages:

1. Collection of distributed telemetry
2. Normalization using the Elastic Common Schema
3. Fault-tolerant ingestion via Apache Kafka
4. Temporal indexing
5. Incremental graph construction
6. Evaluation of the risk function
7. Contextualized alert generation

4.2 Computational Complexity

We use a balanced data structure indexed by timestamp to maintain events in chronological order.

The computational complexity of the process is as follows:

• Event insertion: $O(\log n)$
• Query over a time window $\Delta t$: $O(\log n + k)$
• Graph construction: $O(n \log n)$

This property makes the method practical even in high-event-volume environments.

5. Implementation

5.1 Components Used

The reference implementation uses widely adopted industry tools.

• Traffic inspection with Suricata 7.x
• Endpoint telemetry with Sysmon 15
• Memory analysis with Volatility 3
• Portable detection rules with Sigma
• Identification of malicious artifacts with YARA
• Indexing and search with Elastic Stack
• Fault-tolerant ingestion with Apache Kafka

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 the following:

• NTP synchronization across sensors
• Encrypted transmission of logs
• Network segmentation for critical sensors
• Appropriate log retention for investigations

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)

In addition, we used a complementary controlled scenario of malware execution in an isolated laboratory.

6.2 Metrics

We used standard metrics in intrusion detection for evaluation.

• 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 show a consistent improvement in F1‑score and a reduction in redundant alerts compared with isolated sensors.

We conducted a statistical comparison using stratified cross-validation and the Wilcoxon test, observing significant differences with $p < 0.05$.

7. Case Study

7.1 Enterprise Environment

We simulated an attack chain involving initial compromise, webshell deployment, command-and-control beaconing, and credential extraction from LSASS memory.

Temporal correlation made it possible to reconstruct the full progression of the intrusion from events distributed across network logs, endpoints, and memory artifacts.

7.2 Healthcare Environment

In a scenario emulating hospital infrastructure, we modeled phishing followed by obfuscated PowerShell execution and an exfiltration attempt over HTTPS.

Integration of telemetry from proxy, Sysmon, and Suricata made it possible to identify the TTP chain corresponding to ATT&CK technique T1041 (Exfiltration Over C2 Channel).

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

8. Discussion

The principal contribution of the proposed architecture lies in the introduction of semantic constraints during the correlation process. By restricting valid transitions between events according to ATT&CK, we substantially reduced the occurrence of spurious event combinations.

However, several limitations remain. The model depends on the quality of the mapping between observable events and ATT&CK techniques, a process that can introduce semantic uncertainty. Furthermore, in environments with extremely high event volumes, additional sampling or aggregation strategies may be necessary.

Despite these constraints, the method offers important advantages in terms of interpretability and alignment with operational incident response practice.

9. Conclusion

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

The experimental results and case studies suggest that the time-graph-based approach is a promising direction for detection systems in complex enterprise environments.

Future work includes integration with Graph Neural Networks for structural inference in dynamic graphs, the application of formal verification techniques to validate detection policies, and long-term evaluation in large-scale production environments.

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