Validating DDoS Detection Algorithms for Denial of Wallet Attacks in Serverless Architectures

In the era of cloud computing and serverless architectures, the security of applications and services has become a critical challenge. Serverless computing, often referred to as function as a service (FaaS), is a cloud computing model that allows developers to build and run applications without the need to manage traditional server infrastructure. Serverless architectures have gained popularity in cloud computing due to their flexibility and ability to scale automatically based on demand. These architectures are based on executing functions without the need to manage the underlying infrastructure. Denial of wallet (DoW) attacks refer to a type of cyberattack that aims to exploit and exhaust the financial resources of an organization by triggering excessive costs or charges within their cloud or serverless computing environment, exploiting characteristics such as the pay-as-you-go model, auto-scaling, limited control, and cost amplification. This research aims to assess existing methods for detecting distributed denial of service (DDoS) attacks and extend their application to detect denial of wallet (DoW) threats, leveraging a dataset tailored to serverless architectures. We investigate various strategies and techniques that employ entropy, machine learning and deep learning algorithms to enable early detection of DDoS and DoW attacks in serverless environments. This research provides insights into the options that are available for detecting DoW attacks in serverless environments, allowing security professionals and developers to make decisions on the most appropriate solutions to protect their applications and cloud services.