The emergence of the serverless paradigm, embodied by AWS Lambda functions, has revolutionized the landscape of cloud computing. This model empowers users to offload server management tasks, allowing them to focus their efforts on core business logic while achieving substantial cost savings. However, this transition to serverless exposes significant vulnerabilities, especially in terms of security. This article delves into the specific security challenges associated with AWS Lambda functions, with a focus on major threats such as malicious code injection, sensitive data leaks, DDoS attacks, excessive privileges, vulnerable dependencies, and certificate issues. Our investigation, centered around the AWS Lambda platform, thoroughly analyzes these challenges by identifying underlying mechanisms and inherent risks. We review the state of the art solutions from the literature while examining the strategies adopted by AWS and the industry to enhance security. By implementing these solutions on an AWS server, we concretely illustrate possible protective measures. In this paper, we aims to provide a comprehensive understanding of security issues in the context of Lambda functions, paving the way for recommendations and research directions to bolster the resilience of this essential serverless cloud technology.
Smart contracts are one of the most successful applications of blockchain technology, providing the foundation for a wide range of real-world blockchain applications and occupying a crucial position within the blockchain ecosystem. Hyperledger Fabric, as a influential permissoned blockchain system, warrants in-depth research into the security of its smart contracts. This paper begins by elucidating the sources of security threats to Hyperledger Fabric smart contracts, detailing specific security threats in terms of language inconsistency, external inconsistency, read-write logic, and system security. Subsequently, it provides an overview of research progress in smart contract vulnerability detection techniques, covering feature code matching, symbolic execution, fuzz testing, and intermediate code representation. Based on existing work, it summarizes current methods for detection and evaluation. Finally, drawing upon the summary of existing research efforts, it discusses the challenges faced and potential avenues for future research in the domain of Hyperledger Fabric smart contract vulnerability detection.Related research also facilitates the extension to other heterogeneous permissioned blockchains.
In the context of rapidly evolving cloud service systems and blockchain ecosystems, certain lightweight edge client nodes responsible for continuous generation or transmission of streaming data often exhibit limited computational prowess. Traditional dynamic integrity audit schemes, which rely on one-time initialization frameworks and incur high client-side over-heads, are ill-suited to this scenario. To address these challenges, this paper introduces Iterative Proof of Retrievability (IPoR), an integrity verification scheme for periodic incremental data via optimistic tags generation. We propose a publicly verifiable tripartite IPoR system framework comprised of SDC, EAP and SSP, where the majority of the stream data tagging process is attributed to the SSP responsible for data storage. To ensure the verifiability of the EAP's stream data tag generation process, we introduce a fraud proof based on SMT, the latter substantially decreasing the computational load on public validators. We formally prove under the random oracle model that IPoR satisfies storage integrity and tag generation completeness. Theoretical performance analysis and experimental results demonstrate that our IPoR construction can help clients save over 90% on initial computational overhead.
In the cloud industry, eBPF (extended Berkeley Packet Filter) security technology is one of the most popular and influential technologies in the Linux kernel in recent years. With the rapid development of networking and cloud-native technologies, eBPF is extensively applied in network and security, performance analysis, container and cloud-native environments, operations and troubleshooting, as well as observability of application systems. This paper focuses on the practical application of eBPF technology in cloud environments for ensuring the secure operation, event-driven security handling capability, and performance hotspots observation of transaction systems. The integration of eBPF technology significantly enhances efficiency and reduces costs across the development, testing, and operational phases of systems, providing effective means for security optimization, assisting in testing, and facilitating fault localization and troubleshooting during secure production operations. Moreover, eBPF plays a crucial role in handling security events in cloud environments.
With the increase in the scale of power systems, the development of measurement technology, and the decrease in costs, the amount of data in power systems is showing a rapid growth trend, gradually acquiring the characteristics of big data. Making full use of big data to improve the planning, operation, and control of power systems has received increasing attention. How to evaluate the quality of big data is an important issue worthy of study. There have been considerable research reports on data quality improvement techniques such as data cleaning, data integration, and similar record detection. However, research on data quality evaluation is still in its infancy. Against this background, a comprehensive evaluation method for the quality of power big data is proposed based on the characteristics of power systems and the quality characteristics of power big data. Firstly, an index system for evaluating the quality of power big data is constructed. Then, for the time-sensitive problem of big data processing, a MapReduce parallel K-means clustering algorithm is used to achieve fast preprocessing of big data sample sets. Afterwards, the objective weights of various data sets are calculated using entropy weight method, and the gray evaluation method is used to determine the level of data quality. On this basis, a comprehensive evaluation of sample data sets is realized.
