Evidence-Oriented Architecture for Modern DFIR: Multi-Layer Correlation, Mathematical Formalization of Timelines, and Automated Orchestration in Hybrid Environments

1. Introduction

Digital Forensics and Incident Response (DFIR) has established itself as an essential discipline for organizations confronted with advanced persistent threats (APT), Ransomware-as-a-Service (RaaS) campaigns, and the automated exploitation of critical vulnerabilities. The increasing adoption of hybrid architectures — integrating on-premises infrastructure, cloud services, and containerized environments — significantly expands the attack surface and forensic complexity (BEHL; BEHL, 2017; SCARFONE; SOULE, 2020).

Historically, digital forensic investigations relied on the static acquisition of disk images, the manual analysis of artifacts, and the manual correlation of event logs. However, such approaches prove insufficient in environments characterized by computational elasticity, ephemeral instances, and massive telemetry volumes (CASEY, 2019). Modern environments generate data across multiple layers: network, endpoint, identity, cloud services, and containerized workloads.

Widely documented incidents show how rapidly attacks can escalate after initial exploitation. The Log4Shell vulnerability (CVE-2021-44228), for example, enabled remote code execution at scale within hours of its public disclosure (NIST, 2021). Similarly, the vulnerability CVE-2023-23397 in Microsoft Outlook allowed the silent exfiltration of NTLM hashes via specially crafted messages (MITRE, 2023).

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

Against this background, the following central technical question arises: How can the correlation of heterogeneous evidence be formalized and automated in order to reconstruct complex attack chains with temporal precision and reduced investigative ambiguity?

We formulate three research questions:

RQ1: How can the reconstruction of forensic timelines in distributed and heterogeneous environments be mathematically modeled?

RQ2: What is the quantitative impact of multilevel correlation on reducing mean time to investigation (MTTI) and lowering false positives?

RQ3: How can established security tools be integrated into a verifiable, scalable, and operationally practical architecture for Security Operations Centers?

The main contributions of this paper are:

• C1: We propose a mathematical formalization based on directed acyclic graphs for reconstructing attack chains.
• C2: We develop a multilevel architecture for the collection, normalization, and automated correlation of evidence.
• C3: We empirically demonstrate quantitative gains in a hybrid environment using scenarios aligned with MITRE ATT&CK.

2. Background and Related Work

Digital forensics comprises the application of scientific methods to the identification, preservation, analysis, and presentation of digital evidence (CARRIER, 2005). This field has evolved significantly over the past two decades, accompanying both the transformation of system architectures and the techniques employed by attackers.

The NIST guide SP 800-61r2 defines a widely recognized incident response model structured into four main 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 include a mathematical formalization for the systematic reconstruction of events.

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

Category	Example tools	Type of telemetry
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 for representing malicious activity and analyzing dependencies between events (HASSAN et al., 2019). Provenance computing systems record causal relationships between processes, files, and network flows, thereby enabling the identification of anomalous behavior.

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

Another relevant recent trend is the use of eBPF-based telemetry (extended Berkeley Packet Filter), which enables efficient instrumentation of the Linux kernel for the capture of system-level security events (GREGG, 2020). This form of instrumentation significantly expands forensic visibility.

3. Threat Model and Problem Formalization

We assume an attacker whose capabilities align with the tactics documented 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 over encrypted channels (T1041).

We model the totality of events observed in the system as:

$$ E = {e_1, e_2, …, e_n} $$

Each event $e_i$ is defined as a tuple:

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

where:

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

We then construct a directed graph:

$$ G = (V, A) $$

where $V = E$ and $A$ represents 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:

• process-parent relationship,
• continuation of a network session,
• reuse of credentials,
• access to the same artifact.

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

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

This model allows attacks to be represented as trajectories in temporal graphs.

4. Proposed Methodology

The proposed architecture follows a pipeline of five main layers:

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

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

Event normalization uses the Elastic Common Schema (ECS), enabling fields such as IP, user, process, and file hash to be standardized (ELASTIC, 2023).

The algorithm for constructing the temporal graph is described 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 consists of:

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

5. Implementation

The experimental implementation uses the following technology stack:

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

Example of a Sigma rule for identifying suspicious access to the LSASS process:

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

The experimental infrastructure was operated in a six-node Kubernetes cluster, with each node providing 32 vCPU 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 data analyzed reached approximately 1.2 TB.

Metric	Baseline (SIEM)	Proposed architecture
Average MTTI	14.8 h	8.5 h
False positives	18.3%	13.2%
Correctly reconstructed chains	61.4%	92.8%

We observed a reduction in average investigation time of 42.7% and a decrease in the false-positive rate of approximately 28%.

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

7. Case Study

We conducted a simulation of a multi-stage ransomware attack consisting of the following phases:

1. exploitation of the ProxyShell vulnerability on an Exchange server;
2. deployment of a web shell;
3. credential dumping via LSASS memory dump;
4. lateral movement using PsExec;
5. data exfiltration via the Rclone tool.

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

The main observations include:

• correlation between memory events and network traffic was decisive for identifying the credential dumping phase;
• inconsistencies in time synchronization between hosts can cause timeline reconstruction errors;
• automation via SOAR significantly reduced the manual triage effort.

8. Discussion

The results obtained show that the mathematical formalization of event reconstruction significantly reduces investigative ambiguity. Graph-based representations make it possible to capture complex dependencies between events that would be difficult to identify through log correlation alone.

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

Furthermore, end-to-end encryption and covert channels can reduce the visibility of malicious activity at the network level.

For Security Operations Centers, our results suggest three central practical implications: the integration of network and endpoint telemetry, the standardization of logs using schemas such as ECS, and the adoption of investigative automation through SOAR platforms.

9. Conclusion

We have presented an evidence-oriented architecture for digital forensic investigation in modern hybrid environments. The proposed formal model uses directed acyclic graphs to represent causal relationships between events, thereby enabling the structured reconstruction of attack chains.

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

Future research may examine integration with machine learning techniques for automatic prioritization of the most likely attack chains, as well as the development of formal methods for evidentiary validation of digital evidence in legal contexts.

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