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The growing interest in cybersecurity has significantly increased articles designing and implementing various Cyber Deception (CYDEC) mechanisms. This trend reflects the urgent need for new strategies to address cyber threats effectively. Since its emergence, CYDEC has established itself as an innovative defense against attackers, thanks to its proactive and reactive capabilities, finding applications in numerous real-life scenarios. Despite the considerable work devoted to CYDEC, the literature still presents significant gaps. In particular, there has not been (i) a comprehensive analysis of the main components characterizing CYDEC, (ii) a generic classification covering all types of solutions, nor (iii) a survey of the current state of the literature in various contexts. This article aims to fill these gaps through a detailed review of the main features that comprise CYDEC, developing a comprehensive classification taxonomy. In addition, the different frameworks used to generate CYDEC are reviewed, presenting a more comprehensive one. Existing solutions in the literature using CYDEC, both without Artificial Intelligence (AI) and with AI, are studied and compared. Finally, the most salient trends of the current state of the art are discussed, offering a list of pending challenges for future research., Comment: 38 pages

Function-as-a-Service (FaaS) struggles with burst-parallel jobs due to needing multiple independent invocations to start a job. The lack of a group invocation primitive complicates application development and overlooks crucial aspects like locality and worker communication. We introduce a new serverless solution designed specifically for burst-parallel jobs. Unlike FaaS, our solution ensures job-level isolation using a group invocation primitive, allowing large groups of workers to be launched simultaneously. This method optimizes resource allocation by consolidating workers into fewer containers, speeding up their initialization and enhancing locality. Enhanced locality drastically reduces remote communication compared to FaaS, and combined with simultaneity, it enables workers to communicate synchronously via message passing and group collectives. This makes applications that are impractical with FaaS feasible. We implemented our solution on OpenWhisk, providing a communication middleware that efficiently uses locality with zero-copy messaging. Evaluations show that it reduces job invocation and communication latency, resulting in a 2$\times$ speed-up for TeraSort and a 98.5% reduction in remote communication for PageRank (13$\times$ speed-up) compared to traditional FaaS., Comment: 14 pages, 11 figures, article preprint

Cybersecurity is developing rapidly, and new methods of defence against attackers are appearing, such as Cyber Deception (CYDEC). CYDEC consists of deceiving the enemy who performs actions without realising that he/she is being deceived. This article proposes designing, implementing, and evaluating a deception mechanism based on the stealthy redirection of TCP communications to an on-demand honey server with the same characteristics as the victim asset, i.e., it is a clone. Such a mechanism ensures that the defender fools the attacker, thanks to stealth redirection. In this situation, the attacker will focus on attacking the honey server while enabling the recollection of relevant information to generate threat intelligence. The experiments in different scenarios show how the proposed solution can effectively redirect an attacker to a copied asset on demand, thus protecting the real asset. Finally, the results obtained by evaluating the latency times ensure that the redirection is undetectable by humans and very difficult to detect by a machine.

With the escalating complexity and volume of genomic data, the capacity of biology institutions' HPC faces limitations. While the Cloud presents a viable solution for short-term elasticity, its intricacies pose challenges for bioinformatics users. Alternatively, serverless computing allows for workload scalability with minimal developer burden. However, porting a scientific application to serverless is not a straightforward process. In this article, we present a Variant Calling genomics pipeline migrated from single-node HPC to a serverless architecture. We describe the inherent challenges of this approach and the engineering efforts required to achieve scalability. We contribute by open-sourcing the pipeline for future systems research and as a scalable user-friendly tool for the bioinformatics community., Comment: 6 pages, published at 9th International Workshop on Serverless Computing (WoSC '23), December 11-15, 2023, Bologna, Italy

Over the course of the past two decades, a substantial body of research has substantiated the viability of utilising cardiac signals as a biometric modality. This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network (CNN) is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals. The proposed identification system is evaluated in multiple databases, achieving up to 99.84\% accuracy on healthy subjects, 97.09\% on patients with cardiovascular diseases, and 97.89% on mixed populations including both healthy and arrhythmic patients. The system also performs robustly under varying activity conditions, achieving 91.32% accuracy in scenarios involving different physical activities. These consistent and reliable results, with low error rates such as a FAR of 0.01% and FRR of 0.157% in the best cases, demonstrate the method's significant advancement in subject identification within healthcare systems. By considering patients' cardiovascular conditions and activity levels, the proposed approach addresses gaps in the existing literature, positioning it as a strong candidate for practical applications in real-world healthcare settings.

This paper presents the end-to-end QoS approach to provide performance guarantees followed in the SELENE platform, a high-performance RISC-V based heterogeneous SoC for safety-related real-time systems. Our QoS approach includes smart interconnect solutions for buses and NoCs, along with multicore interference-aware statistics units to, cooperatively, achieve end-to-end QoS., Comment: 4 pages, 3 figures, work presented on FORECAST workshop of HIPEAC 2022

Serverless computing, in particular the Function-as-a-Service (FaaS) execution model, has recently shown to be effective for running large-scale computations. However, little attention has been paid to highly-parallel applications with unbalanced and irregular workloads. Typically, these workloads have been kept out of the cloud due to the impossibility of anticipating their computing resources ahead of time, frequently leading to severe resource over- and underprovisioning situations. Our main insight in this article is, however, that the elasticity and ease of management of serverless computing technology can be a key enabler for effectively running these problematic workloads for the first time in the cloud. More concretely, we demonstrate that with a simple serverless executor pool abstraction one can achieve a better cost-performance trade-off than a Spark cluster of static size built upon large EC2 virtual machines. To support this conclusion, we evaluate three irregular algorithms: Unbalanced Tree Search (UTS), Mandelbrot Set using the Mariani-Silver algorithm and Betweenness Centrality (BC) on a random graph. For instance, our serverless implementation of UTS is able to outperform Spark by up to 55% with the same cost. We also show that a serverless environment can outperform a large EC2 in the BC algorithm by a 10% using the same amount of virtual CPUs. This provides the first concrete evidence that highly-parallel, irregular workloads can be efficiently executed using purely stateless functions with almost zero burden on users i.e., no need for users to understand non-obvious system-level parameters and optimizations. Furthermore, we show that UTS can benefit from the FaaS pay-as-you-go billing model, which makes it worth for the first time to enable certain application-level optimizations that can lead to significant improvements (e.g. of 41%) with negligible increase in cost., Comment: 14 pages, 9 figures

Serverless functions provide high levels of parallelism, short startup times, and "pay-as-you-go" billing. These attributes make them a natural substrate for data analytics workflows. However, the impossibility of direct communication between functions makes the execution of workflows challenging. The current practice to share intermediate data among functions is through remote object storage (e.g., IBM COS). Contrary to conventional wisdom, the performance of object storage is not well understood. For instance, object storage can even be superior to other simpler approaches like the execution of shuffle stages (e.g., GroupBy) inside powerful VMs to avoid all-to-all transfers between functions. Leveraging a genomics pipeline, we show that object storage is a reasonable choice for data passing when the appropriate number of functions is used in shuffling stages., Comment: 2 pages, 1 figure

Access transparency means that both local and remote resources are accessed using identical operations. With transparency, unmodified single-machine applications could run over disaggregated compute, storage, and memory resources. Hiding the complexity of distributed systems through transparency would have great benefits, like scaling-out local-parallel scientific applications over flexible disaggregated resources in the Cloud. This paper presents a performance evaluation where we assess the feasibility of access transparency over state-of-the-art Cloud disaggregated resources for Python multiprocessing applications. We have interfaced the multiprocessing module with an implementation that transparently runs processes on serverless functions and uses an in-memory data store for shared state. To evaluate transparency, we run in the Cloud four unmodified applications: Uber Research's Evolution Strategies, Baselines-AI's Proximal Policy Optimization, Pandaral.lel's dataframe, and ScikitLearn's Hyperparameter tuning. We compare execution time and scalability of the same application running over disaggregated resources using our library, with the single-machine Python multiprocessing libraries in a large VM. For equal resources, applications efficiently using message-passing abstractions achieve comparable results despite the significant overheads of remote communication. Other shared-memory intensive applications do not perform due to high remote memory latency. The results show that Python's multiprocessing library design is an enabler towards transparency: legacy applications using efficient disaggregated abstractions can transparently scale beyond VM limited resources for increased parallelism without changing the underlying code or architecture.

As more applications are being moved to the Cloud thanks to serverless computing, it is increasingly necessary to support the native life cycle execution of those applications in the data center. But existing cloud orchestration systems either focus on short-running workflows (like IBM Composer or Amazon Step Functions Express Workflows) or impose considerable overheads for synchronizing massively parallel jobs (Azure Durable Functions, Amazon Step Functions). None of them are open systems enabling extensible interception and optimization of custom workflows. We present Triggerflow: an extensible Trigger-based Orchestration architecture for serverless workflows. We demonstrate that Triggerflow is a novel serverless building block capable of constructing different reactive orchestrators (State Machines, Directed Acyclic Graphs, Workflow as code, Federated Learning orchestrator). We also validate that it can support high-volume event processing workloads, auto-scale on demand with scale down to zero when not used, and transparently guarantee fault tolerance and efficient resource usage when orchestrating long running scientific workflows., Comment: 17 pages, 17 figures, preprint submitted to Future Generation Computer Systems. arXiv admin note: substantial text overlap with arXiv:2006.08654

Within the next 10 years, advances on resource disaggregation will enable full transparency for most Cloud applications: to run unmodified single-machine applications over effectively unlimited remote computing resources. In this article, we present five serverless predictions for the next decade that will realize this vision of transparency -- equivalent to Tim Wagner's Serverless SuperComputer or AnyScale's Infinite Laptop proposals., Comment: arXiv admin note: text overlap with arXiv:2006.01251

Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental cryptographic primitive in which users register into a key generation centre and receive a public/private key pair each. After that, any subset of these users can compute a shared key without any interaction. Nowadays, IoT devices suffer from a high number and large size of messages exchanged in the Key Management Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate the airtime and latency of messages transferred between IoT devices. MP-NIKE schemes can be realized by using multilinear maps. There are several attempts for constructing multilinear maps based on indistinguishable obfuscation, lattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes are inefficient in terms of computation cost and memory overhead. Besides, several attacks have been recently reported against CRT-based and lattice-based multilinear maps. There is only one modular exponentiation-based MP-NIKE scheme in the literature which has been claimed to be both secure and efficient. In this article, we present an attack on this scheme based on the Euclidean algorithm, in which two colluding users can obtain the shared key of any arbitrary subgroup of users. We also propose an efficient and secure MP-NIKE scheme. We show how our proposal is secure in the random oracle model assuming the hardness of the root extraction modulo a composite number., Comment: 14 pages, 3 figures