Evidence-Driven Architectures for Modern DFIR: Multi-Level Correlation, Mathematical Formalization of Timelines, and Automated Orchestration in Hybrid Environments

1. Introduction

Digital Forensics and Incident Response (DFIR) has become a core discipline in organizations facing advanced persistent threats (APTs), ransomware-as-a-service (RaaS) campaigns, and automated exploitation of critical vulnerabilities. The increasing adoption of hybrid architectures—integrating on-premises infrastructure, cloud services, and containerized environments—has significantly expanded the attack surface and increased investigative complexity (BEHL; BEHL, 2017; SCARFONE; SOULE, 2020).

Historically, digital forensic investigations relied on static disk image acquisition, manual artifact analysis, and hand-crafted correlation of event logs. However, in environments with computational elasticity, short-lived instances, and massive telemetry volumes, these methods have proven insufficient (CASEY, 2019). Modern environments generate data from multiple layers: network, endpoint, identity, cloud services, and containerized workloads.

Numerous publicly disclosed incidents have shown how quickly attacks can escalate after initial exploitation. For example, the Log4Shell vulnerability (CVE-2021-44228) enabled large-scale remote code execution within hours of public disclosure (NIST, 2021). Similarly, CVE-2023-23397 in Microsoft Outlook enabled silent NTLM hash exfiltration through specially crafted messages (MITRE, 2023).

Threat intelligence reports indicate that attacker dwell time in compromised environments remains substantial. The M-Trends 2023 report reported a global median dwell time of 16 days, evidencing persistent gaps in incident detection and investigation (MANDIANT, 2023).

In this context, the central technical problem is: how can heterogeneous evidence be formalized and automatically correlated to reconstruct complex attack chains while preserving temporal precision and reducing investigative ambiguity?

We propose three research questions:

RQ1: How can forensic timeline reconstruction be mathematically modeled in distributed and heterogeneous environments?

RQ2: What is the quantitative impact of multi-layer correlation on reducing mean time to investigate (MTTI) and lowering false positives?

RQ3: How can mature security tools be integrated into a verifiable, scalable, and operationally viable Security Operations Center architecture?

The main contributions of this paper are as follows:

• C1: We propose a mathematical formalization based on directed acyclic graphs for attack chain reconstruction.
• C2: We develop a multilayer architecture for evidence ingestion, normalization, and automated correlation.
• C3: We empirically demonstrate quantitative gains in a hybrid environment in scenarios aligned with MITRE ATT&CK.

2. Background and Related Work

Digital forensic computing involves the application of scientific methods to identify, preserve, analyze, and present digital evidence (CARRIER, 2005). Over the past two decades, the field has evolved significantly alongside changes in computer system architectures and the techniques used by attackers.

The NIST SP 800-61r2 guide defines a widely adopted incident response model consisting of four major phases: preparation, detection and analysis, containment/eradication, and post-incident activity (CICHONSKI et al., 2012). Although this model provides important operational guidance, it does not offer a mathematical formalization for systematic incident reconstruction.

Modern security monitoring and analysis tools produce large volumes of telemetry. The most relevant categories include:

Category	Tool Examples	Telemetry Type
SIEM	Elastic, Splunk	Log correlation and aggregation
EDR	Microsoft Defender, CrowdStrike	Endpoint telemetry
NDR	Zeek, Suricata	Network traffic analysis
Memory Forensics	Volatility 3	RAM artifact analysis
CNAPP	Prisma Cloud, Wiz	Cloud workload telemetry

In recent years, research has begun to explore graph-based models to represent malicious activity and analyze dependencies between events (HASSAN et al., 2019). Provenance computing systems record causal relationships among processes, files, and network flows, enabling the identification of anomalous behavior.

Despite these advances, important gaps remain. Many approaches focus on real-time detection but do not robustly handle retrospective reconstruction of attack chains with forensic value. In addition, the heterogeneity of data sources—including cloud provider logs such as AWS CloudTrail and Azure Activity Logs—makes consistent correlation across events difficult.

Another important recent trend is the adoption of eBPF (extended Berkeley Packet Filter)-based telemetry, which enables efficient instrumentation of the Linux kernel for system-level security event collection (GREGG, 2020). This instrumentation substantially improves investigative visibility.

3. Threat Model and Problem Formalization

We assume an attacker with capabilities consistent with the tactics recorded in the MITRE ATT&CK framework (MITRE, 2023), including:

• remote command execution (T1059),
• credential dumping (T1003),
• lateral movement via SMB or WinRM (T1021),
• data exfiltration through encrypted channels (T1041).

We model the complete set of observed events in the system as:

$$ E = {e_1, e_2, …, e_n} $$

Each event $e_i$ is defined as a five-tuple:

$$ e_i = (t_i, s_i, d_i, a_i, c_i) $$

where:

• $t_i$ denotes the event timestamp;
• $s_i$ denotes the source;
• $d_i$ denotes the destination;
• $a_i$ denotes the action performed;
• $c_i$ contains additional context (file hashes, PID, user, IP address, or instance identifier).

We then construct a directed graph:

$$ G = (V, A) $$

where $V = E$, and $A$ denotes the set of causal relationships between events.

Formally:

$$ A = {(e_i, e_j) \mid t_i < t_j \land related(e_i, e_j)} $$

The function $related(e_i, e_j)$ evaluates semantic dependencies between events, such as:

• parent-child process relationships,
• network session continuity,
• credential reuse,
• access to the same artifact.

The investigative objective is to find a chain $C$ that maximizes the likelihood of the observed causal sequence:

$$ \max_{C \subseteq G} P(C \mid E) $$

This model enables representing attacks as trajectories in a temporal graph.

4. Proposed Method

The proposed architecture follows a pipeline with five main layers:

1. Telemetry collection
2. Event normalization
3. Temporal correlation
4. Probabilistic inference
5. Orchestration and response

The collection layer integrates multiple data sources, including network sensors (Suricata, Zeek), endpoint telemetry (Sysmon), and cloud logs (AWS CloudTrail). In Linux environments, additional events can be captured through eBPF.

Event normalization uses the Elastic Common Schema (ECS) to standardize fields such as IPs, users, processes, and file hashes (ELASTIC, 2023).

The graph construction algorithm is shown below:

Algorithm 1: BuildAttackGraph(E)
Input: Event set E
Output: Directed acyclic graph G

1: Sort E by timestamp
2: Initialize graph G
3: for each event e_i in E:
4:   for each event e_j where t_j > t_i:
5:       if Correlate(e_i, e_j):
6:           AddEdge(G, e_i, e_j)
7: return G

The computational complexity is:

• Initial sorting: $O(n \log n)$
• Correlation within a time window $k$: $O(nk)$

5. Implementation

The experimental implementation used the following technology stack:

Component	Tool	Function
SIEM	Elastic Stack 8.x	Ingestion and correlation
NDR	Suricata 7	Traffic detection
NDR	Zeek 6	Network behavior analysis
Endpoint	Sysmon v15	Process telemetry
Forense	Volatility 3	Memory analysis
SOAR	TheHive 5 + Cortex	Response orchestration

A sample Sigma rule for identifying suspicious access to the LSASS process is as follows:

logsource:
  product: windows
detection:
  selection:
    EventID: 10
    TargetImage: 'lsass.exe'
condition: selection

The experimental infrastructure ran on a six-node Kubernetes cluster, with each node equipped with 32 vCPUs and 128 GB of memory.

6. Experimental Evaluation

The experimental environment used 37 scenarios based on the MITRE ATT&CK framework. The total volume of analyzed data was approximately 1.2 TB.

Metric	Baseline (SIEM)	Proposed Architecture
Mean MTTI	14.8 hours	8.5 hours
False positive rate	18.3%	13.2%
Correctly reconstructed chains	61.4%	92.8%

We observed an average reduction of 42.7% in investigation time and an approximate 28% reduction in false positives.

The system processed approximately 18,000 events per second, with an average correlation latency of 3.4 seconds.

7. Case Study

We simulated a multi-stage ransomware attack with the following phases:

1. Exploitation of the ProxyShell vulnerability on an Exchange server;
2. Deployment of a webshell;
3. Credential dumping via LSASS dump;
4. Lateral movement using PsExec;
5. Data exfiltration using Rclone.

Reconstruction of the attack graph made it possible to identify the full attack chain approximately 11 minutes after the initial event ingestion.

Key observations include:

• Correlation between memory events and network traffic was critical for identifying the credential dumping stage;
• Inconsistent time synchronization among hosts may introduce errors into timeline reconstruction;
• Automation through SOAR substantially reduced manual triage workload.

8. Discussion

The results indicate that mathematical formalization of incident reconstruction can significantly reduce investigative ambiguity. Graph-based representations capture complex event dependencies that are difficult to identify through log correlation alone.

However, some limitations remain. The effectiveness of the model depends on the quality and completeness of the collected telemetry. Environments with insufficient logging or limited data retention may undermine attack chain reconstruction.

In addition, end-to-end encryption and covert channels may reduce visibility of malicious activity at the network layer.

For Security Operations Centers, our results suggest three main practical implications: integration of network and endpoint telemetry, log standardization through schemas such as ECS, and adoption of investigation automation via SOAR platforms.

9. Conclusion

We presented an evidence-driven architecture for digital forensic investigations in modern hybrid environments. The proposed formal model uses directed acyclic graphs to represent causal relationships among events, enabling structured reconstruction of attack chains.

The experimental evaluation demonstrated significant improvements in key operational metrics, including reduced mean investigation time and increased incident reconstruction accuracy.

Future work may explore integration with machine learning techniques to automatically prioritize the most likely attack chains, as well as formal methods for validating the evidentiary weight of digital evidence in legal contexts.

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