Formal and Operational Architecture for Threat Detection and Containment in Multi-Cloud Environments Based on Unified Telemetry and Adversarial Modeling

1. Introduction

Cloud computing has established itself as the dominant paradigm for contemporary digital infrastructure. Organizations across different sectors have migrated critical workloads to environments based on infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Market estimates indicate that the global volume of public cloud services has surpassed hundreds of billions of dollars annually, reflecting the centrality of this model to corporate digital transformation (GARTNER, 2023).

Despite the benefits of scalability, elasticity, and automation, cloud environments introduce new classes of security risks. The attack surface becomes highly dynamic due to the continuous creation and destruction of resources, exposure of administrative APIs, and extensive use of federated identities and temporary tokens. Widely documented incidents illustrate this scenario. The 2019 Capital One breach exploited a Server-Side Request Forgery (SSRF) vulnerability to access the EC2 instance metadata service and obtain temporary credentials associated with overly permissive IAM policies (SEN; SHARMA, 2020). Similarly, the Log4Shell vulnerability (CVE-2021-44228) demonstrated how widely used components can introduce systemic risks in containerized and serverless environments (NIST, 2022).

In multi-cloud environments, the problem becomes even more complex. Each provider exposes different auditing and telemetry mechanisms, such as AWS CloudTrail, Azure Activity Logs, and Google Cloud Audit Logs. In addition, modern workloads use orchestration platforms such as Kubernetes, which introduce additional layers of logs and security events (BURNS; BEDA; HIGHTOWER, 2019). In many organizations, this data is collected by traditional SIEM tools, but analyzed in a fragmented manner, without explicit representation of the relationships among identities, resources, and permissions.

This fragmentation limits the ability to identify attacks that exploit permission chains distributed across services. Techniques widely observed in cloud environments, such as T1078 (Valid Accounts) and T1550 (Use of Web Tokens), often involve multiple lateral escalation steps that are not detected when events are analyzed in isolation (MITRE, 2023).

In this context, this work investigates the following research questions:

RQ1: How can the attack surface of cloud environments be mathematically formalized so as to enable efficient inference of lateral escalation paths?

RQ2: Is it possible to unify heterogeneous telemetry from multiple providers into a common analytical model oriented toward MITRE ATT&CK tactics and techniques?

RQ3: What is the operational impact of this approach on detection rate and reduction of mean time to respond to incidents?

Our main contributions are threefold. First, we propose a formal model based on weighted directed graphs that represents identities, resources, and authorization policies in multi-cloud environments. Second, we develop incremental correlation algorithms capable of integrating events from multiple telemetry sources. Third, we experimentally evaluate the proposed architecture in a realistic multi-cloud environment containing hundreds of instances and multiple attack scenarios.

The remainder of the paper is organized as follows. Section 2 presents background and related work. Section 3 describes the threat model and problem formalization. Section 4 presents the proposed methodology. Section 5 describes the implementation of the architecture. Section 6 presents the experimental evaluation. Section 7 describes a realistic case study. Section 8 discusses implications and limitations. Finally, Section 9 presents the conclusions.

2. Background and Related Work

The National Institute of Standards and Technology defines cloud computing as a model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (MELL; GRANCE, 2011). These resources include networks, servers, storage, and services that can be rapidly provisioned with minimal administrative effort.

Formally, we can represent a cloud environment by two fundamental sets:

$R = {r_1, r_2, …, r_n}$ representing computational resources and
$I = {i_1, i_2, …, i_m}$ representing identities.

Authorization relationships can be modeled as:

$A \subseteq I \times R$

where each pair $(i,r)$ indicates that identity $i$ has some level of access to resource $r$.

The academic literature on cloud security can be grouped into three main categories.

First, log-analysis-based approaches. Works such as those by Almorsy, Grundy, and Ibrahim analyze audit records to detect anomalous behavior in cloud infrastructures (ALMORSY; GRUNDY; IBRAHIM, 2016). These methods depend heavily on the quality of the available telemetry.

Second, configuration-analysis-based approaches. Tools and studies aimed at detecting misconfigurations in infrastructure as code have become particularly relevant with the adoption of DevOps and infrastructure automation (RAHMAN; WILLIAMS, 2020).

Third, graph-modeling-based approaches. Recent works use graphs to represent privilege relationships and identify potential attack paths in cloud environments (LI; YUAN; LI, 2019). However, much of this work focuses on a single provider or does not integrate operational telemetry in near real time.

In addition, commercial solutions such as AWS GuardDuty and Microsoft Defender for Cloud use behavioral analysis and machine learning techniques, but operate as proprietary systems without an open formalization of their analytical models (SCARFONE; SOUPPAYA, 2020).

Therefore, important gaps remain in the literature, including the absence of a rigorous mathematical formalization of the multi-cloud attack surface and the lack of comparative experimental evaluations involving different telemetry sources.

3. Threat Model and Problem Formalization

We consider an adversary model consistent with scenarios observed in real cloud security incidents. The attacker initially has access to a valid credential or temporary token obtained through leakage, phishing, or accidental exposure in public repositories (MELARA et al., 2021).

The adversary’s capabilities include:

• Use of valid credentials to access cloud APIs (T1078).
• Exploitation of application vulnerabilities to access instance metadata.
• Lateral escalation through overly permissive IAM policies.
• Use of session tokens or federated identity services.

We model the system as a directed graph:

$G = (V, E)$

where the set of vertices is:

$V = I \cup R$

and the set of edges is defined as:

$E = {(i,r) \mid i \text{ has permission over } r}$.

Each edge has an associated weight $w(e)$ representing the estimated probability of exploitation or abuse of that permission relationship.

We then define the risk function of a path $p$ in the graph as:

$$ Risk(p) = \prod_{e \in p} w(e) $$

where $p$ represents a sequence of permissions that allows the adversary to reach a sensitive resource.

The central problem consists of identifying paths $p$ such that:

$$ Risk(p) > \theta $$

where $\theta$ represents a risk threshold defined by the organization’s security policy.

Additional operational criteria include:

• integrity and authenticity of the collected logs;
• ability to correlate multiple event sources;
• detection in less than five minutes after the initial event.

4. Proposed Methodology

The proposed architecture consists of four main layers that enable ingestion, normalization, correlation, and incident response.

Collection layer: responsible for ingesting telemetry from different providers and sensors. It includes AWS CloudTrail, Azure Monitor, Google Cloud Audit Logs, VPC Flow Logs, and network sensors based on Suricata.

Normalization layer: events are converted into a common schema based on the Elastic Common Schema (ECS), enabling correlation across different sources.

Correlation layer: normalized events are used to dynamically update a permission graph stored in Neo4j. Sigma-based detection rules are used to map events to MITRE ATT&CK techniques.

Response layer: when a high-risk path is detected, automated playbooks are executed by SOAR platforms for credential revocation, instance isolation, or API key blocking.

Algorithm 1 – Incremental graph construction

Algorithm UpdateGraph(Event e)
Input: event e
1: Parse(e)
2: Extract identity i and resource r
3: if (i,r) not in E then
4:    AddEdge(i,r)
5: UpdateWeight(i,r)
6: return G

The amortized complexity of the algorithm is $O(1)$ per event, assuming efficient indexing in the graph database.

Algorithm 2 – Identification of high-risk paths

Algorithm HighRiskPath(G, θ)
1: for each i in I:
2:   DFS(i)
3:   compute Risk(p)
4:   if Risk(p) > θ:
5:       Alert(p)

The complexity of each query is $O(|V| + |E|)$.

5. Implementation

The architecture was implemented on a Kubernetes cluster running version 1.27, distributed across 12 nodes with 64 vCPU and 256 GB of memory each. The environment was deployed in a distributed manner across three cloud providers to simulate multi-cloud scenarios.

The technological stack used is presented below.

Component	Technology	Function
Event ingestion	Apache Kafka	Distributed event pipeline
Log analysis	Elastic Stack 8.x	Indexing and search
Graph database	Neo4j 5.x	Permission modeling
Network sensor	Suricata 7.0	IDS/IPS detection
Rule-based detection	Sigma	Rule normalization
Orchestration	Kubernetes	Workload management

Example Sigma rule used to detect AssumeRole abuse in AWS:

logsource:
  product: aws
  service: cloudtrail
detection:
  selection:
    eventName: AssumeRole
  condition: selection
level: high

During the experiments, the pipeline processed approximately 18,000 events per second, with an average ingestion latency of 1.3 seconds.

6. Experimental Evaluation

The experimental evaluation was conducted in a multi-cloud environment containing 420 instances distributed across AWS, Microsoft Azure, and Google Cloud Platform. A total of 36 attack scenarios inspired by techniques documented in MITRE ATT&CK were executed.

The metrics used include:

• true positive rate (TPR);
• false positive rate (FPR);
• mean time to respond (MTTR);
• computational overhead.

System	TPR	FPR	MTTR
Traditional SIEM	81.2%	7.4%	41.7 min
Proposed architecture	94.7%	3.8%	16.3 min

The reduction in MTTR was approximately 61%, indicating that graph-based correlation facilitates rapid identification of attack chains.

The average observed CPU overhead was 6.8%, indicating that the architecture can be deployed in corporate environments without significant impact on operational performance.

7. Case Study

To evaluate the behavior of the architecture in a realistic scenario, we simulated an incident involving accidental exposure of AWS credentials in a public repository.

The attack sequence was as follows:

1. The red team identifies an access key using a secrets-scanning tool.
2. The attacker executes the command aws sts get-caller-identity to validate the credential.
3. Next, they use existing permissions to assume an additional role.
4. The system identifies the sequence of events corresponding to techniques T1078 and T1550.
5. An automated playbook revokes the compromised credential and applies a blocking policy.

The total time between detection and containment was approximately 11 minutes.

8. Discussion

The results indicate that graph-based modeling offers significant advantages for security analysis in complex cloud environments. By explicitly representing relationships between identities and resources, it becomes possible to identify escalation chains that would not be visible in analyses based solely on isolated events.

However, some limitations should be considered. First, the effectiveness of the system depends on the quality and completeness of the collected telemetry. Environments with incomplete logging may reduce correlation capability. In addition, very large environments may generate graphs with millions of vertices, increasing the computational cost of queries.

Another limitation concerns the fact that the experimental scenarios, although realistic, were conducted in a controlled environment. Additional studies in large-scale corporate environments are necessary to validate the generalizability of the results.

9. Conclusion

This work presented a formal and operational architecture for threat detection and containment in multi-cloud environments based on unified telemetry and adversarial modeling. We demonstrated that the integration of multiple event sources with graph-based representation enables the identification of lateral escalation paths associated with MITRE ATT&CK techniques.

The experiments indicated significant improvements over traditional SIEM approaches, with increased detection rate and a substantial reduction in mean response time. These results suggest that the formal modeling of the attack surface can contribute meaningfully to the defense of modern cloud infrastructures.

Future investigations may explore the use of machine learning methods to dynamically estimate the weights $w(e)$ associated with permission relationships, as well as advanced graph-analysis techniques for identifying complex adversarial patterns.

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