Organizations use Infrastructure as Code (IaC) to define, configure, and manage infrastructure through code. Terraform is commonly used to define infrastructure resources, including security groups and their network access rules.
Terraform security group rules control which network connections are permitted to reach infrastructure resources. If an inbound rule permits access from unrestricted source address ranges, such as 0.0.0.0/0 or ::/0, sensitive services may be exposed to unnecessary network access. The security risk depends on the permitted port, protocol, service, and intended network-access requirements.
In this use case, XGBoost-based machine learning is combined with Checkov security scanning to identify and prioritize unrestricted network access in Terraform security group rules. Python extracts relevant configuration features, including source address ranges, port ranges, protocols, and traffic direction. XGBoost classifies configurations using labelled secure/insecure examples, while Checkov independently checks Terraform files for supported security-policy violations.
The practical assessment is performed using VirtualBox, Ubuntu Linux, and Kali Linux. Ubuntu hosts the project environment and a controlled test service. Kali Linux is used to assess the authorized laboratory target using Nmap. A local firewall rule can be configured to reproduce the intended network-access behavior for practical testing. This local test demonstrates network exposure behavior; it does not create an actual cloud security group.
A controlled attack scenario is prepared by defining an intentionally unrestricted inbound rule in a local Terraform configuration. The corresponding test environment is configured to demonstrate whether the designated service is reachable under the selected laboratory network and firewall settings.
The initial results from XGBoost, Checkov, and Nmap are reviewed together. A remediation recommendation is then prepared to restrict unnecessary network access. After the correction is reviewed and applied, Checkov and the network-access assessment are repeated to compare the initial and final security states.
A Streamlit dashboard presents the classification results, security findings, risk priorities, remediation recommendations, and validation status.
Application/Service: Terraform Security Group Rules
Terraform is the Infrastructure-as-Code tool used to define the security group configurations assessed in this use case.
Security group rules may allow broader inbound network access than the application or service requires. Manually reviewing configuration files can make it difficult to identify, classify, and prioritize these weaknesses consistently.
Unrestricted source ranges do not automatically make every rule vulnerable. However, allowing unnecessary access to sensitive services can increase the attack surface and may expose those services to unauthorized connection attempts.
The proposed solution combines XGBoost-based configuration classification, Checkov security scanning, controlled network exposure testing, finding correlation, risk prioritization, and post-remediation validation.
Specific Attack: Unrestricted Network Access Through Terraform Security Group Rules
The controlled attack scenario evaluates whether an intentionally unrestricted inbound rule could permit unnecessary access to a designated test service.
A local Terraform configuration is prepared with an unrestricted source address range. Its security characteristics are analysed by XGBoost and Checkov. A corresponding network-access condition is then established on the authorized Ubuntu test environment so that Kali Linux can assess the actual reachability of the designated service.
Nmap is used to identify reachable ports and services on the authorized lab target. The assessment is limited to the isolated laboratory and does not involve attacking an external system.
AI-Assisted Infrastructure-as-Code Security Analysis and Network Access Validation
The primary security concept is to combine machine-learning-based configuration classification with rule-based Infrastructure-as-Code security scanning and practical network-access testing.
XGBoost classifies Terraform security group configurations using features extracted from labelled secure/insecure samples. Checkov independently identifies supported security-policy violations. Kali Linux and Nmap provide network-level evidence about the reachability of the authorized laboratory target.
The security concept defines the overall security strategy. The following are the specific defensive mechanisms implemented.
Relevant security group attributes are extracted from Terraform configuration files.
The model classifies configurations according to patterns learned from labelled samples.
Checkov scans Terraform configurations for supported security-policy violations.
Nmap is used from Kali Linux to assess reachable ports and services on the authorized Ubuntu laboratory target.
XGBoost predictions, Checkov results, and the original Terraform rule are reviewed together.
The source range, port, protocol, intended service, and potential impact are reviewed.
Unnecessary inbound access is restricted according to the intended network-access requirements.
The corrected Terraform file is rescanned, and the controlled network-access test is repeated.
Streamlit presents the findings, model predictions, risk priorities, and remediation status.
XGBoost is used to classify security group configuration samples based on extracted features.
Python implements the data-processing, model-training, detection, and reporting workflow.
Terraform provides the configuration files containing the security group rules under assessment.
Checkov scans Terraform files for supported security-policy violations.
Nmap is used from Kali Linux to assess the reachable ports and services of the authorized laboratory target.
Ubuntu hosts the project environment and controlled test service.
Kali Linux provides the network security testing environment.
VirtualBox provides the isolated laboratory environment for Ubuntu and Kali Linux.
The Ubuntu firewall can be used to establish a controlled network-access condition corresponding to the test scenario.
Streamlit presents the assessment results through a web-based dashboard.
Publicly available Terraform configuration examples and labelled secure/insecure samples are used for model development and evaluation.
A local Infrastructure-as-Code security assessment environment is established using VirtualBox, Ubuntu Linux, and Kali Linux for controlled configuration analysis and network-access testing.
Terraform security group configurations containing restricted and intentionally unrestricted inbound rules are prepared for assessment without deploying vulnerable cloud infrastructure.
Publicly available Terraform configuration examples and labelled secure/insecure samples are used to develop and evaluate the XGBoost classification model without specifying a particular dataset name.
XGBoost classifies extracted security group configuration features, while Checkov independently identifies applicable Infrastructure-as-Code security-policy violations.
A controlled unrestricted network-access scenario is assessed using a corresponding local firewall configuration, an Ubuntu test service, and Nmap from Kali Linux.
XGBoost predictions, Checkov findings, Terraform rule details, and network-testing evidence are correlated to validate findings and prioritize security risks.
Reviewed remediation recommendations are applied to Terraform configurations to restrict unnecessary inbound access according to the intended security requirements.
Post-remediation Checkov scans, XGBoost classification, and Nmap assessments provide before-and-after evidence of security changes, with local firewall results evaluated separately from cloud security group validation.
A Streamlit dashboard presents configuration findings, classification results, risk priorities, remediation recommendations, model evaluation metrics, and validation status.
The complete assessment process provides a repeatable local workflow for identifying Infrastructure-as-Code security misconfigurations, prioritizing risks, recommending remediation, and evaluating security improvements.
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