As a lightweight, flexible, and high-performance operating system virtualization, containers are used to speed up the big data platform. However, due to the imperfection of the resource isolation mechanism and the property of shared kernel, the meltdown and spectre attacks can lead to information leakage of kernel space and coresident containers. In this article, a noise-resilient and real-time detection system, named ContainerGuard, is proposed to detect meltdown and spectre attacks in the container-based big data platform. ContainerGuard uses a nonintrusive manner to collect lifecycle multivariate time-series performance event data of processes in containers and then uses ensemble of variational autoencoders as generative neural networks to learn the robust representations of normal patterns. Therefore, ContainerGuard meets the urgent need for information protection in the container-based big data platform. Our evaluations using real-world datasets show that ContainerGuard achieves excellent detection performance and only introduces about 4.5% of running performance overhead to the platform.

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.

This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests for thin-tailed distributional assumptions imposed on these two components. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and applications to four previously studied datasets are provided. In two of these applications, the new tests reject the thin-tailed distributional assumptions such as normal or Laplace, which are commonly imposed in the existing literature.

As the proportion of encrypted traffic increases, it becomes increasingly challenging for network attacks to be discovered. Although existing methods combine unencrypted statistical features, e.g., average packet length, with machine learning algorithms to achieve encrypted malicious traffic detection, it is difficult to escape the influence of artificially forged noise, e.g., adding dummy packets. In this study, we propose a novel encrypted malicious traffic detection method named RobustDetector (RD) for obfuscated malicious traffic detection. The core of the proposed method is to use the dropout mechanism to simulate the process of original features being disturbed. By introducing noise during the training phase, the robustness of the model is improved. To validate the effectiveness of RobustDetector, we conducted extensive experiments using public datasets. Our results demonstrate that RobustDetector achieves an average F1-score of 90.63% even when random noise is introduced to the original traffic with a probability of 50%. This performance underscores the potential of our proposed method in addressing the challenges of obfuscated malicious traffic detection.

In the evolving landscape of complex business ecosystems and their digital platforms, an increasing number of business Application Programming Interfaces (APIs) are encountering challenges in ensuring optimal authorization control. This challenge arises due to factors such as programming errors, improper configurations, and sub-optimal business processes. While security departments have exhibited proficiency in identifying vulnerabilities and mitigating certain viral or adversarial incursions, the safeguarding of comprehensive business processes remains an intricate task. This paper introduces a novel paradigm, denoted as the Low-Intervention Security Embedding Architecture (LiSEA), which empowers business applications to enhance the security of their processes through judicious intervention within business APIs. By strategically incorporating pre- and post-intervention checkpoints, we devise a finely grained access control model that meticulously assesses both the intent of incoming business requests and the outcomes of corresponding responses. Importantly, these advancements are seamlessly integrated into the existing business codebase. Our implementation demonstrates the effectiveness of LiSEA, as it adeptly addresses eight out of the ten critical vulnerabilities enumerated in the OWASP API Security Top 10. Notably, when the number of threads is less than 200, LiSEA brings less than 20 msec of latency to the business process, which is significantly less than the microservice security agent based on the API gateway.

This paper discusses optimizing desktop image quality and bandwidth consumption in remote IoT GUI desktop scenarios. Remote desktop tools, which are crucial for work efficiency, typically employ image compression techniques to manage bandwidth. Although JPEG is widely used for its efficiency in eliminating redundancy, it can introduce quality loss with increased compression. Recently, deep learning-based compression techniques have emerged, challenging traditional methods like JPEG. This study introduces an optimized RFB (Remote Frame Buffer) protocol based on a convolutional neural network (CNN) image compression algorithm, focusing on human visual perception in desktop image processing. The improved RFB protocol proposed in this paper, compared to the unoptimized RFB protocol, can save 30–80% of bandwidth consumption and enhances remote desktop image quality, as evidenced by improved PSNR and MS-SSIM values between the remote desktop image and the original image, thus providing superior desktop image transmission quality.