Formal and Operational Architecture for Evidence-Driven Incident Response with Multilayer Correlation and Automated Orchestration

1. Introduction

Incident Response (IR) is one of the central pillars of contemporary cybersecurity, encompassing technical and organizational processes aimed at the identification, analysis, containment, eradication, and recovery following security events (CICHONSKI et al., 2012). In modern corporate environments, the expansion of distributed architectures, cloud services, and hybrid infrastructures has resulted in massive volumes of operational telemetry. Medium and large organizations frequently record more than $10^7$ security events per day, originating from heterogeneous sensors, including intrusion detection systems, operating system logs, endpoint protection platforms, and corporate applications.

Despite the growing adoption of SIEM platforms and behavioral detection solutions, effective event correlation remains a significant technical challenge. Empirical reports indicate that the global average time to identify and contain incidents remains high across several industrial sectors, often exceeding hundreds of days in complex attacks (IBM, 2023; VERIZON, 2023). Widely documented episodes, such as the SolarWinds supply chain compromise and the massive exploitation of the Log4Shell vulnerability (CVE‑2021‑44228), demonstrated that sophisticated attacks often manifest through distributed and apparently unrelated signals across the infrastructure.

Offensive techniques described in the MITRE ATT&CK framework, such as T1059 (Command and Scripting Interpreter), T1003 (Credential Dumping), and T1021 (Remote Services), are typically executed sequentially and with a low detectable signature (MITRE, 2023). As a result, isolated monitoring tools generate fragmented alerts that rarely capture the full progression of an attack.

The central problem therefore lies in the fragmentation of evidence. Network IDSs, such as Suricata, produce alerts based on signatures or heuristic detection. Endpoint sensors, such as Sysmon and Wazuh, generate detailed logs on process execution and system activity. Memory forensic tools, such as Volatility, are often employed only after manual escalation of the incident. The absence of a formal model for temporal and causal correlation among these sources results in operational overload for analysts and a high rate of false positives.

In this scenario, we formulate three research questions:

• RQ1: How can the correlation of distributed security events across multiple monitoring layers be formally modeled?
• RQ2: What is the quantitative impact of automated orchestration on reducing the operational metrics MTTD and MTTR?
• RQ3: Is it possible to increase the forensic quality of collected evidence without significantly increasing computational cost?

The main contributions of this work are:

• C1: Formal definition of the incident correlation problem through a probabilistic temporal graph.
• C2: Proposal of a multilayer operational architecture integrated with automated orchestration mechanisms (SOAR).
• C3: Quantitative experimental evaluation based on attack scenarios aligned with MITRE ATT&CK.

2. Background and Related Work

The NIST SP 800‑61 guide establishes the classical incident response model, structured into four main phases: preparation, detection and analysis, containment/eradication, and recovery (CICHONSKI et al., 2012). Although widely adopted in industry, this model describes procedural and organizational workflows, but does not formalize algorithmic mechanisms for automated event correlation.

In the operational context, Bejtlich (2013) emphasizes that investigative effectiveness depends on correlating network telemetry and host data. Modern monitoring tools have significantly expanded operational visibility, but the integrated analysis of these sources remains challenging.

Early academic research explored graph-based alert correlation. Ning and Xu (2003) proposed directed graph models to represent attack sequences inferred from IDS alerts. These models make it possible to identify causal intrusion chains, although they often depend on prior knowledge of offensive strategies.

Subsequently, machine learning–based approaches were applied to anomaly detection in network traffic and system behavior (SOMMER; PAXSON, 2010). However, such techniques face relevant limitations, including difficulty in interpreting results and dependence on labeled datasets.

With the growth of cloud-native architectures and observability pipelines, new correlation tools began integrating logs, metrics, and events into scalable analytical platforms (TURNBULL, 2018). Even so, many implementations remain rule-driven, without explicit mathematical foundations.

Recurring limitations identified in the literature include:

• absence of formal modeling of temporal causality between events;
• limited integration between forensic analysis and real-time detection;
• scarcity of quantitative evaluations using operational metrics such as MTTD and MTTR.

This work addresses these gaps by combining mathematical correlation modeling with an operational architecture based on tools widely adopted in practice.

3. Threat Model and Problem Formalization

We consider an adversary $A$ with capabilities consistent with advanced intrusion campaigns observed in corporate environments. The adversary may exploit remote code execution vulnerabilities, execute malicious scripts, perform lateral movement, and extract credentials from memory.

We model the set of observed events as

$$ E = {e_1, e_2, …, e_n} $$

Each event $e_i$ is defined as:

$$ e_i = (t_i, s_i, h_i, \tau_i, \alpha_i) $$

where:

• $t_i$ represents the event timestamp,
• $s_i$ represents the sensor source,
• $h_i$ identifies the affected host,
• $\tau_i$ represents the associated ATT&CK technique,
• $\alpha_i$ represents a vector of observable attributes.

We define a temporal graph:

$$ G = (V, E’, T) $$

in which $V$ corresponds to the set of correlated events and $E’$ represents directed edges indicating potential causal relationships.

Each edge receives a probabilistic weight:

$$ w_{ij} = P(e_j | e_i) $$

This weight represents the probability that event $e_j$ is a causal consequence of event $e_i$.

The correlation problem consists of maximizing:

$$ \max \sum_{(i,j)} w_{ij} $$

subject to the temporal constraint

$$ |t_j - t_i| < \Delta $$

where $\Delta$ defines the maximum temporal correlation window.

Operational performance criteria are defined as:

• correct correlation rate ≥ 90%;
• false positive rate ≤ 5%;
• processing latency ≤ 5 seconds for each batch of 10,000 events.

4. Proposed Methodology

4.1 General Architecture

The proposed architecture is composed of five functional layers:

Layer	Function	Tools
Collection	Telemetry capture	Suricata, Sysmon
Aggregation	Ingestion and normalization	Logstash, Kafka
Correlation	Probabilistic temporal analysis	Elastic Stack, Python
Orchestration	Response automation	TheHive, Cortex
Forensics	Evidence collection and analysis	Volatility, YARA

This architecture makes it possible to transform raw events into structured incident hypotheses.

4.2 Temporal Correlation Algorithm

Input: event set E, temporal window Δ
Output: correlated graph G

1. Sort events by timestamp
2. For each event e_i in E:
3.   Select events e_j such that |t_j − t_i| < Δ
4.   Compute semantic similarity between e_i and e_j
5.   Estimate probabilistic weight w_ij
6.   If w_ij > θ then
7.       add edge (i,j) to the graph
8. Return G

The computational complexity of the algorithm is approximately $O(n \log n)$ for initial sorting and $O(nk)$ for temporal neighborhood evaluation.

4.3 Automated Orchestration

Input: incident I

1. Classify severity based on the event graph
2. Select the corresponding playbook
3. Execute remote memory acquisition
4. Apply YARA scanning
5. Isolate compromised host via security API
6. Record evidence in the case management system

Automation significantly reduces latency between detection and response.

5. Implementation

The experimental implementation used the following technology stack:

Component	Version
Suricata	6.0
Wazuh	4.4
Elastic Stack	8.x
Apache Kafka	3.x
TheHive	5
Volatility	3

Sysmon sensors were configured to capture relevant events, including:

• Event ID 1 (Process Creation)
• Event ID 3 (Network Connection)
• Event ID 10 (Process Access)
• Event ID 11 (File Creation)

Integration between tools was performed through REST webhooks and log ingestion pipelines.

Example of a YARA rule used to identify indicators associated with Mimikatz:

rule Mimikatz_Indicator {
    strings:
        $mz = "mimikatz"
    condition:
        $mz
}

6. Experimental Evaluation

The experimental environment consisted of a controlled infrastructure containing:

• 12 Ubuntu 22.04 servers
• 200 Windows 11 endpoints
• analysis nodes with 64 GB of RAM

Attacks were simulated using the Atomic Red Team framework, aligned with MITRE ATT&CK.

Aggregated results:

Metric	Baseline	Proposed
MTTD	18.4 min	11.5 min
MTTR	42.7 min	24.7 min
F1-score	0.71	0.91

The observed standard deviation for MTTD was ±2.1 minutes. Statistical tests indicated significance with $p < 0{,}01$.

7. Case Study

We considered an intrusion scenario composed of the following stages:

1. exploitation of the Log4Shell vulnerability in a web application;
2. establishment of a reverse shell;
3. execution of credential extraction tools;
4. attempted deployment of ransomware.

The observed sequence of events was as follows:

1. Suricata detects HTTP traffic associated with the exploit.
2. Sysmon records anomalous execution of powershell.exe.
3. Wazuh identifies patterns consistent with techniques T1059 and T1003.
4. The SOAR platform automatically triggers the containment playbook.
5. Memory analysis with Volatility confirms the presence of credentials in LSASS.

The total incident containment time was approximately 19 minutes.

8. Discussion

The experimental results indicate that formal modeling based on temporal graphs significantly improves the ability to correlate distributed events. The probabilistic representation of relationships between events reduces analytical noise and improves the explainability of the identified incidents.

However, some limitations must be considered. The effectiveness of the model depends on the quality of the collected telemetry and is sensitive to the absence of critical logs or instrumentation failures. In addition, attacks that use end-to-end encryption may reduce visibility at the network layers.

Another relevant aspect concerns scalability. Large-scale environments may produce hundreds of thousands of events per second, requiring distributed processing and storage architectures.

9. Conclusion

This work presented a formal and operational architecture for incident response based on probabilistic event correlation and automated orchestration. The proposed mathematical model makes it possible to represent causal relationships between security events across different layers of the infrastructure, reducing evidence fragmentation.

Experiments conducted in a controlled environment demonstrated significant improvements in the operational metrics MTTD and MTTR, as well as increased incident classification accuracy.

The results suggest that the integration of formal modeling, response automation, and evidence-driven forensic analysis can substantially increase the operational efficiency of security teams.

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