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

1. Introduction

Cloud computing, as formalized by the National Institute of Standards and Technology (NIST) in specification SP 800‑145, is characterized by five essential properties: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service (MELL; GRANCE, 2011). These properties enable scalability and cost optimization for organizations of different sizes. However, they simultaneously expand the attack surface and redistribute security responsibilities between providers and consumers according to the shared responsibility model.

The adoption of multi-cloud architectures — in which an organization simultaneously uses multiple providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform — has become common practice for strategic, regulatory, and technical reasons. Such an approach reduces dependence on a single vendor, enables the selection of specialized services, and facilitates compliance with data sovereignty requirements. However, this strategy introduces semantic heterogeneity between equivalent services, differences in identity and access models, and disparities in auditing and observability mechanisms.

Several public incidents illustrate the risks inherent in this scenario. The 2019 Capital One breach involved exploitation of an SSRF vulnerability that enabled unauthorized access to the AWS Instance Metadata Service (IMDSv1), resulting in the acquisition of temporary credentials associated with the compromised instance (UNITED STATES DISTRICT COURT, 2020). Using these credentials, the adversary was able to enumerate and access storage buckets containing sensitive data. The incident highlighted the importance of identity governance, the principle of least privilege, and integrated visibility over API activity.

Another widely studied event was the Log4Shell vulnerability (CVE‑2021‑44228), present in the Apache Log4j library and exploited globally starting in December 2021 (NVD, 2021). The vulnerability allowed remote code execution via JNDI resolution in manipulated logs, affecting Java applications distributed across on-premises and cloud-native environments. The episode revealed weaknesses in software supply chains and the difficulty of inventorying dependencies in highly distributed environments.

Threat intelligence reports published by providers and independent organizations indicate that abuse of valid credentials constitutes one of the predominant vectors in contemporary attacks against cloud environments (MENDIOLA et al., 2020; MICROSOFT, 2023). The MITRE ATT&CK framework systematizes these techniques through an operational taxonomy that includes, among others, T1078 (Valid Accounts), T1098 (Account Manipulation), and T1530 (Data from Cloud Storage Object) (MITRE, 2023).

In multi-cloud environments, telemetry heterogeneity constitutes a central obstacle to efficient detection. Control plane logs such as AWS CloudTrail, Azure Activity Logs, and Google Cloud Audit Logs have distinct schemas and varying levels of granularity. Additionally, network telemetry and container runtime events often remain isolated from control plane logs, making it difficult to correlate across layers.

This fragmentation results in two recurring operational problems. First, attack chains that traverse multiple layers — identity, network, and workload execution — become difficult to reconstruct. Second, detection systems based solely on isolated rules tend to produce a high number of false positives in the absence of global context.

Given this scenario, we investigate the following research questions:

RQ1: How can threat detection in multi-cloud environments be mathematically formalized using temporal graphs of heterogeneous events?

RQ2: What is the impact of unifying control plane, network, and runtime telemetry on the detection of attack chains represented in the MITRE ATT&CK framework?

RQ3: To what extent does this approach reduce mean time to detect while maintaining operationally acceptable false positive rates?

Our central hypothesis is that formal modeling of attack chains as annotated temporal graphs, combined with the semantic unification of telemetry from multiple system layers, enables measurable detection gains without compromising operational accuracy.

2. Background and Related Work

2.1 Security in Cloud Environments

Research on cloud computing security can be organized into different strands. One of the earliest concerns in the literature was isolation between virtual machines in multi-tenant environments. Zhang et al. (2012) demonstrated the feasibility of side-channel attacks between co-resident virtual machines capable of extracting RSA cryptographic keys.

Another important line of research addresses analysis and verification of access control policies. Attribute-based systems (ABAC) and formal verification techniques have been explored to identify inconsistencies and violations of the principle of least privilege in complex environments (HU; KUHN; XIE, 2015).

In the specific context of intrusion detection in the cloud, Modi et al. (2013) conducted a comprehensive survey of existing approaches, highlighting scalability challenges and distributed event collection. However, many of these studies predate the widespread adoption of cloud-native architectures and container orchestration platforms such as Kubernetes.

More recently, research has begun to explore attack modeling through graphs and causal dependencies. Hasan et al. (2019) investigated methods for detecting advanced persistent threats using probabilistic models over event graphs.

2.2 Runtime Observability and eBPF

The observability of containerized workloads has evolved significantly with the use of eBPF-based technologies. eBPF enables secure instrumentation of the Linux kernel for collecting system events with low overhead (HENDRIX; HØILAND‑JØRGENSEN, 2021). Tools such as Falco use this technology to monitor system calls and detect anomalous behavior in containers and hosts.

In parallel, distributed log analysis systems such as the Elastic Stack have become widely used for ingesting, normalizing, and querying large volumes of telemetry (ELASTIC, 2023). The Elastic Common Schema provides a common field model that facilitates correlation across different data sources.

Despite these technological advances, a gap remains between the theoretical formalization of attack detection and operational implementation in multi-cloud environments. Our work seeks to fill this gap by proposing an explicit mathematical model combined with a concrete operational architecture.

3. Threat Model and Problem Formalization

3.1 Adversary Model

We consider adversaries with different operational capabilities:

• C1: abuse of valid credentials (OAuth tokens, API keys, or IAM credentials);
• C2: code execution in cloud workloads after vulnerability exploitation;
• C3: enumeration of resources and policies via management APIs;
• C4: manipulation of access policies or creation of privileged identities;
• C5: exfiltration of data from storage services or databases.

We assume that providers maintain the basic integrity of audit logs and that the adversary does not compromise the underlying hypervisor or break widely used cryptographic primitives.

3.2 Event Modeling

We define the set of observable events as $E = {e_1, e_2, …, e_n}$. Each event is represented as

$$ e_i = (t_i, s_i, a_i, r_i, c_i, \sigma_i) $$

where $t_i$ is the timestamp, $s_i$ the subject, $a_i$ the executed action, $r_i$ the affected resource, $c_i$ the context, and $\sigma_i$ the telemetry source.

3.3 Temporal Graphs

We define an annotated temporal graph $G = (V, E_g, \lambda)$ where each vertex corresponds to an observed event. An edge $(v_i, v_j)$ is created if

$$ 0 < t_j - t_i \leq \Delta t \land Correl(e_i, e_j) $$

The detection function is defined as

$$ f(G) = \begin{cases} 1 & \text{if } G \text{ contains a malicious subgraph} \ 0 & \text{otherwise} \end{cases} $$

This formulation makes it possible to express attack chains as structural patterns in event graphs.

4. Proposed Architecture

The proposed architecture is organized into multiple layers of collection, processing, and analysis.

• Control plane collection: AWS CloudTrail, Azure Activity Logs, and Google Cloud Audit Logs.
• Network telemetry: Suricata exporting events in EVE JSON format.
• Runtime telemetry: eBPF-based Falco.
• Ingestion pipeline: Apache Kafka for distributed event transport.
• Semantic normalization: Logstash and Elastic Ingest Pipelines with mapping to the Elastic Common Schema.
• Storage and query: Elasticsearch.
• Detection and correlation: Sigma rules and structured queries.
• Response orchestration: TheHive and Cortex.

This architecture enables the consolidation of heterogeneous events into a common semantic model, facilitating the construction of event graphs.

5. Experimental Evaluation

5.1 Experimental Environment

The experiments were conducted in a multi-cloud environment involving three providers:

• AWS (us-east-1)
• Azure (West Europe)
• Google Cloud Platform (us-central1)

Kubernetes clusters ran synthetic workloads and attack scenarios derived from the MITRE ATT&CK framework using the Atomic Red Team tool (RED CANARY, 2023).

5.2 Baselines

Baseline	Description
B1	Control plane logs only (CloudTrail)
B2	Multi-cloud logs without network or runtime telemetry
B3	Full proposed architecture

5.3 Metrics

The metrics used include:

• MTTD (Mean Time To Detect)
• Recall
• Precision
• False positive rate (FPR)

Confidence intervals were estimated by bootstrap with 1000 resamples.

5.4 Results

Architecture	Mean MTTD	Recall	Precision	FPR
B1	18.6 min	0.62	0.91	0.08
B2	11.3 min	0.74	0.90	0.09
B3	5.2 min	0.89	0.92	0.07

We observed a significant reduction in mean detection time by integrating telemetry from multiple layers. Attack chains involving container execution and DNS-based exfiltration were detected more consistently when runtime and network events were included in the analysis.

5.5 Ablation Study

Removing network telemetry significantly reduced the ability to identify DNS-based exfiltration. The absence of runtime events reduced the detection of interactive executions in containers.

6. Case Studies

6.1 Financial Scenario

We simulated compromise of an AWS credential exposed in a public repository. The adversary performed bucket enumeration, attached a privileged policy, and began an exfiltration attempt. The architecture correlated identity, API, and workload activity events, enabling early identification of the attack chain.

6.2 Healthcare Scenario

In an Azure-GCP hybrid environment, we detected a sequence involving anomalous login, generation of a federated token, and massive access to storage objects containing medical images. Multi-cloud correlation allowed complete reconstruction of the event chain.

7. Discussion

Graph-based temporal modeling provides an explicit basis for analyzing causal dependencies among distributed events. This approach improves the explainability of detection decisions when compared to systems based purely on isolated signatures.

However, some limitations remain. The approach depends on the integrity and availability of provider-supplied logs. In addition, large-scale environments can generate substantial data volumes, requiring adequate infrastructure for storage and processing.

Another relevant limitation concerns adversarial evasion. A sophisticated attacker may attempt to fragment attack chains into time intervals greater than the $\Delta t$ threshold. Future investigations may explore probabilistic models or machine learning to mitigate this problem.

8. Conclusion

We presented a formal and operational architecture for threat detection and response in multi-cloud environments based on unified telemetry and temporal graph modeling. We demonstrated that the integration of control plane logs, network telemetry, and runtime events produces significant gains in the detection of complex attack chains.

The experimental results indicate a substantial reduction in mean detection time and a consistent improvement in recall when compared with approaches based exclusively on API logs.

This convergence between mathematical formalization and operational engineering represents an important step toward strengthening the security of modern cloud infrastructures.

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