Related papers: Serverless AI Security: Attack Surface Analysis an…
Offloading computation from user devices to nodes with processing capabilities at the edge of the network is a major trend in today's network/service architectures. At the same time, serverless computing has gained a huge traction among the…
Model inference systems are essential for implementing end-to-end data analytics pipelines that deliver the benefits of machine learning models to users. Existing cloud-based model inference systems are costly, not easy to scale, and must…
The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application…
Assurance for artificial intelligence (AI) systems remains fragmented across software supply-chain security, adversarial machine learning, and governance documentation. Existing transparency mechanisms - including Model Cards, Datasheets,…
The serverless computing paradigm promises many desirable properties for cloud applications - low-cost, fine-grained deployment, and management-free operation. Consequently, the paradigm has underwent rapid growth: there currently exist…
Machine learning is being increasingly used by individuals, research institutions, and corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the…
Serverless computing (also known as functions as a service) is a new cloud computing abstraction that makes it easier to write robust, large-scale web services. In serverless computing, programmers write what are called serverless…
Machine Learning (ML) systems are vulnerable to adversarial examples, particularly those from query-based black-box attacks. Despite various efforts to detect and prevent such attacks, ML systems are still at risk, demanding a more…
Serverless computing is increasingly popular because of its lower cost and easier deployment. Several cloud service providers (CSPs) offer serverless computing on their public clouds, but it may bring the vendor lock-in risk. To avoid this…
Serverless computing has become a new trending paradigm in cloud computing, allowing developers to focus on the development of core application logic and rapidly construct the prototype via the composition of independent functions. With the…
Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating the need for infrastructure management. On cloud provider…
The serverless computing model has evolved as one of the key solutions in the cloud for fast autoscaling and capacity planning. In edge computing environments, however, the serverless model is challenged by the system heterogeneity and…
Microservice architectures (MSA) are becoming trending alternatives to existing software development paradigms notably for developing complex and distributed applications. Microservices emerged as an architectural design pattern aiming to…
This work presents ATLAS, an LLM-driven framework that bridges standardized threat modeling and property-based formal verification for System-on-Chip (SoC) security. Starting from vulnerability knowledge bases such as Common Weakness…
Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding the client-side…
The serverless computing ecosystem is growing due to interest by software engineers. Beside Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) systems, developer-oriented tools such as deployment and debugging frameworks as well…
Machine learning (ML) is increasingly being deployed in critical systems. The data dependence of ML makes securing data used to train and test ML-enabled systems of utmost importance. While the field of cybersecurity has well-established…
Recently, serverless computing has gained recognition as a leading cloud computing method. Providing a solution that does not require direct server and infrastructure management, this technology has addressed many traditional model problems…
Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains. While existing approaches employ cryptographic techniques such as homomorphic encryption,…
Autonomous agent frameworks built upon large language models (LLMs) are evolving into complex, tool-integrated, and continuously operating systems, introducing security risks beyond traditional prompt-level vulnerabilities. As this paradigm…