Introduction
Serverless computing has emerged as one of the most compelling paradigms in cloud computing. It enables users to quickly run applications and services without managing the underlying server infrastructure. Despite the availability of various commercial and open-source serverless platforms, there are still significant security challenges that need to be addressed.
Security is perhaps the biggest impediment preventing wider adoption of serverless computing. Many businesses and research organizations are cautious about migrating their valuable assets to third-party service providers. Conventional IT infrastructures typically keep data and applications within private data centers or on-premises servers. However, the security measures of external cloud service providers are often opaque to customers. Additionally, the presence of multiple users on shared cloud platforms can lead to distrust between them, further exacerbating security concerns.
In this article, we explore methods to quantify the security of serverless computing platforms. We propose a novel security quantification approach that integrates Attack Trees, Attack-Defense Trees, and a Relative Risk Matrix to assess the probability of attack success. This comprehensive analysis provides valuable insights into the security landscape of serverless computing, empowering stakeholders, including application developers, researchers, and cloud providers, to better understand and enhance the security of these platforms.
Understanding Serverless Computing and OpenFaaS
Serverless computing is a cloud execution model where the cloud provider dynamically manages the allocation and provisioning of servers and other resources, abstracting away the underlying infrastructure from the developer. This paradigm, also known as Function-as-a-Service (FaaS), allows developers to focus on building and deploying specific functions or microservices, rather than managing the complexities of virtual machines, servers, and other infrastructure.
One of the popular open-source serverless frameworks is OpenFaaS. OpenFaaS provides a simple yet flexible interface for developers to deploy functions and microservices, regardless of the underlying infrastructure or container orchestration platform. It supports various container runtimes, including Kubernetes, OpenShift, and Docker Swarm.
The core components of the OpenFaaS architecture are the API Gateway and the Watchdog. The API Gateway handles routing, autoscaling, and metrics monitoring for the deployed functions. The Watchdog is responsible for starting and monitoring the execution of the functions, allowing any binary to be turned into a serverless function.
OpenFaaS also integrates with other open-source tools, such as Prometheus for monitoring and AlertManager for triggering scaling alerts based on function metrics. This comprehensive ecosystem enables developers to build, deploy, and monitor serverless applications efficiently.
Security Quantification Mechanism
To quantify the security of serverless computing platforms, we propose a novel approach that combines Attack Trees, Attack-Defense Trees, and a Relative Risk Matrix.
Attack Trees and Attack-Defense Trees
Attack Trees are a well-established methodology for modeling and analyzing security threats. They use a tree-like structure to represent the different ways a system can be attacked, with the root node representing the overall attack goal and the leaf nodes representing the individual attack steps.
Attack-Defense Trees extend the Attack Tree concept by incorporating defensive measures against the identified attacks. This allows for a more comprehensive analysis of the security landscape, including the effectiveness of countermeasures.
Relative Risk Matrix
The Relative Risk Matrix is a tool we introduce to quantify the probability of attack success. It considers three key factors:
- Vulnerability Severity: The potential impact of a vulnerability if exploited.
- Consequence Impact: The severity of the consequences if an attack is successful.
- Exploitability: The ease with which an attacker can exploit a vulnerability.
By assigning scores to these factors and multiplying them, we obtain a matrix value that represents the overall risk. We then standardize these matrix values using a normal distribution and apply the cumulative distribution function to derive the probability of attack success.
This approach provides a more objective and quantifiable assessment of the security risks in serverless computing platforms, enabling stakeholders to make informed decisions and prioritize security enhancements.
Security Quantification of Serverless Computing
To demonstrate the application of our security quantification approach, we divide the serverless computing architecture into the following layers:
- Cloud Layer: Risks associated with the underlying cloud infrastructure and services.
- Container Infrastructure Layer: Threats related to the container technology and orchestration platforms.
- Serverless Layer: Vulnerabilities specific to the serverless framework and its components.
- Access Layer: Risks stemming from user devices and network access.
For each layer, we construct Attack Trees and Attack-Defense Trees to model the potential threats and countermeasures. We then apply the Relative Risk Matrix to quantify the probability of attack success, both before and after the implementation of defensive measures.
Cloud Layer
In the cloud layer, we identify risks related to data modification and destruction, data leakage, and vulnerabilities in cloud service provider (CSP) APIs. We also consider attacks that target the cloud user’s side, such as credential and secret leakage, as well as external factors like environmental adversities and network faults.
Our analysis shows that the probability of a successful attack on the cloud layer is approximately 99.99%, which is alarmingly high. However, by implementing countermeasures like data backup, access control, and network monitoring, we can reduce the probability of a successful attack to around 98%.
Container Infrastructure Layer
At the container infrastructure layer, we focus on risks associated with container technology and Kubernetes, the popular container orchestration platform. Potential threats include exposed secrets, insecure networking, misconfigured containers, and attacks on the build machine and Kubernetes components.
The initial probability of a successful attack on the container infrastructure layer is also around 99%. By applying defenses such as secret rotation, network encryption, and access control, we can lower the probability of a successful attack to 97%.
Serverless Layer
In the serverless layer, we analyze the security risks within the serverless framework, specifically targeting the OpenFaaS platform. Potential attack vectors include penetration testing on the API Gateway, message hijacking and malicious execution in the NATS streaming component, and vulnerabilities in the provider and watchdog modules.
Our quantification reveals that the probability of a successful attack on the serverless layer is approximately 99%. By implementing countermeasures like access control, encryption, and trusted provider selection, we can reduce the probability of a successful attack to 95%.
Access Layer
The access layer represents the user devices and network access points that interact with the serverless computing platform. While we do not provide a detailed quantification for this layer, we acknowledge the potential risks associated with unauthorized devices, stolen devices, remote control, malware, network interception, and phishing attacks.
Securing the access layer requires a multi-faceted approach, including device management, strong authentication, and comprehensive user education.
Conclusion and Future Directions
Our security quantification analysis of serverless computing platforms paints a concerning picture. Even with the implementation of defensive measures, the probability of a successful attack remains high, around 97% across the different layers. This highlights the critical need for further security enhancements in serverless computing ecosystems.
To address these challenges, we propose the following future research directions:
- Comparative Analysis: Evaluate our security quantification approach against other established methods to validate its effectiveness and identify areas for improvement.
- Empirical Evaluation: Conduct comprehensive penetration testing and vulnerability assessments to validate the identified risks and the efficacy of the proposed countermeasures.
- Minimum Cost Optimization: Explore the integration of cost analysis, such as the minimum cost of attack and minimum cost of defense, to optimize the security strategy while considering budgetary constraints.
- Fault Tree and Reliability Analysis: Incorporate additional analytical techniques, such as Fault Tree analysis and Reliability Block Diagrams, to provide a more holistic security quantification framework.
By addressing these future research directions, we can further enhance the security of serverless computing platforms, empowering cloud providers, application developers, and end-users to confidently adopt and leverage this transformative technology.
