Container technology, (e.g., Docker) is being widely adopted for deploying software infrastructures or applications in the form of container images. Security vulnerabilities in the container images are a primary concern for developing containerized software. Exploitation of the vulnerabilities could result in disastrous impact, such as loss of confidentiality, integrity, and availability of containerized software. Understanding the exploitability and impact characteristics of vulnerabilities can help in securing the configuration of containerized software. However, there is a lack of research aimed at empirically identifying and understanding the exploitability and impact of vulnerabilities in container images. We carried out an empirical study to investigate the exploitability and impact of security vulnerabilities in base-images and their prevalence in open-source containerized software. We considered base-images since container images are built from base-images that provide all the core functionalities to build and operate containerized software. Besides, security vulnerabilities in a base-image can propagate to derived container images, which can host different applications. That means a single exploitable vulnerability in base-images can result in security attacks in several containerized software. Our analysis of a set of 1, 983 unique base-image security vulnerabilities revealed 13 novel findings. These findings are expected to help developers to understand the potential security problems related to base-images and encourage them to investigate base-images from security perspective before developing their applications. For researchers, this study highlights the need of developing tools for mitigating the exploitability of vulnerable base-images.

Container Orchestrator (CO) is a vital technology for managing clusters of containers, which may form a virtualized infrastructure for developing and operating software systems. Like any other software system, securing CO is critical, but can be quite challenging task due to large number of configurable options. Manual configuration is not only knowledge intensive and time consuming, but also is error prone. For automating security configuration of CO, we propose a novel Knowledge Graph based Security Configuration, KGSecConfig, approach. Our solution leverages keyword and learning models to systematically capture, link, and correlate heterogeneous and multi-vendor configuration space in a unified structure for supporting automation of security configuration of CO. We implement KGSecConfig on Kubernetes, Docker, Azure, and VMWare to build secured configuration knowledge graph. Our evaluation results show 0.98 and 0.94 accuracy for keyword and learning-based secured configuration option and concept extraction, respectively. We also demonstrate the utilization of the knowledge graph for automated misconfiguration mitigation in a Kubernetes cluster. We assert that our knowledge graph based approach can help in addressing several challenges, e.g., misconfiguration of security, associated with manually configuring the security of CO.

Background: Docker technology has been increasingly used among software developers in a multitude of projects. This growing interest is due to the fact that Docker technology supports a convenient process for creating and building containers, promoting close cooperation between developer and operations teams, and enabling continuous software delivery. As a fast-growing technology, it is important to identify the Docker-related topics that are most popular as well as existing challenges and difficulties that developers face.

Matrix multiplication is a prime operation in linear algebra and scientific computations. In this paper, Artificial Neural Network-based matrix multiplication is introduced to create a completely new horizon in matrix multiplication technique, due to having non-linear, non-parametric characteristics of Neural Network. The time complexity of the proposed matrix multiplication algorithm based on neural networks is O(log n(n+n2+ n2/2 + log2n)), whereas the time complexity of the best known matrix multiplication algorithm is O(n3/p), where n is the dimension of the matrix and p is the number of processing elements. Besides, Artificial Neural Network being the powerful data-driven, self-adaptive tool, it provides the resultant matrix multiplication with a high degree of accuracy. Through supervised learning, the neural network completes multiplication through addition operation instead of multiplication in solution prediction stage, which evidently reduces required number of Look-Up Table (LUT). The proposed design achieves an improvement of 43.88% and 50.17% over the best known existing approach in terms of number of LUTs and slices required, respectively.