A data publishing deal conducted with anonymous microdata can preserve the privacy of people. However, anonymizing data with multiple records of an individual (1:M dataset) is still a challenging problem. After anonymizing the 1:M microdata, the vertical correlation can be exploited to launch privacy attacks. In this paper, a novel privacy preserving model l(c), l(s)-ANGEL is proposed. To validate the new model, two privacy attacks are presented, namely, aVertical correlation attack (V-c0) and aVulnerable sensitive attribute attack (V-sa) on 1:M datasets, which breach the privacy of individuals. Furthermore, the proposed model is examined through High-Level Petri Nets (HLPNs). Our experiments on three real-world datasets;"INFORMS","YOUTUBE", and "IMDb" demonstrate that the proposedmodel outperforms the state-of-the-art models. Our practices and lessons learned in this work can direct future concrete steps towards Multiple Sensitive Attributes, where we can expand the proposed model to dynamic datasets.

In the evolving landscape of cloud computing, containerized microservices have emerged as a dominant architecture, presenting unique security challenges. This paper introduces a novel security framework, harnessing the power of machine learning, to enhance the detection and response capabilities against misuse in Kubernetes-based microservices environments. Central to our approach is the Dynamic Topology Adjustment (DTA) operator, seamlessly integrated with Kube-OVN's advanced networking features, enabling proactive and dynamic adaptation of the network topology in response to real-time security threats. We implement an AI-driven misuse detection model based on the SGDOneClassSVM algorithm, tailored to analyze network flows within these complex systems. Our framework not only addresses immediate security concerns but also sets a foundation for adaptive, intelligent security management in cloud-based microservices. Experimental results, derived from a specially curated dataset targeting container-specific vulnerabilities, demonstrate the efficacy of our approach in detecting a range of security threats with high accuracy, showcasing its potential as a robust solution for container security in cloud environments.

Container security involves a broad spectrum of concerns, including the security of the operating system, auditing the supply chain and the application security of the running containers. This wide attack surface will also include the security of the container orchestration system and its components once a container orchestration system is introduced to manage the fleet of containers in an environment. In order to advance the research in this field, prior work should be comparable and reproducible. However, we identified a research gap for this aspect; publicly available datasets for container security is sparse and reproducibility of the research output so far is arduous. In this study, we share a dataset consisting of network flows, collected from a Kubernetes cluster. Furthermore, we performed a preliminary analysis on the data as a sanity check to evaluate its quality. By sharing this dataset publicly, we hope to help further studies and establish benchmarks in the field of container networking security.

The rising use of microservice-based software deployment on the cloud leverages containerized software extensively. The security of applications running inside containers, as well as the container environment itself, are critical for infrastructure in cloud settings and 5G. To address security concerns, research efforts have been focused on container security with subfields such as intrusion detection, malware detection and container placement strategies. These security efforts are roughly divided into two categories: rule-based approaches and machine learning that can respond to novel threats. In this study, we survey the container security literature focusing on approaches that leverage machine learning to address security challenges.

Microservices architecture has been praised as a lightweight, modular and robust alternative to monolithic software in recent years with software containerization bringing parallel ideas to the table against bare metal and even virtual machine based software deployment solutions. While containers provide support for agile software development in the cloud, they suffer from security issues due to their lightweight structure not providing isolation as strong as that of virtual machines. This calls for the development of robust intrusion detection systems (IDS) for containers, taking into account their specific vulnerabilities. Existing IDS for containerized software deployments have mainly used host-based syscall monitoring, with only a few utilizing network-based monitoring without justification for the particular sensor used. In this paper, we aim to close this research gap by empirically evaluating the performances of system call and network flow based features in machine learning-based intrusion detection for containers when subjected to the same attacks. Our results show that basing the IDS on the network layer exhibits better performance than the host-based IDS for the investigated vulnerabilities, demonstrating the need for network monitoring for enhanced container security.

The rising area of the Internet of Things (IoT) faces various issues related to the scalability and security of the data produced. Most IoT devices cannot perform complex computations on their own due to resource constraints, which require them to outsource their jobs. Due to the abundance of devices and the IoT network’s heterogeneous nature, there is a large attack surface against IoT systems. During outsourcing jobs, a major factor that affects performance is the delay in data transmission. However, the security issues such as data manipulation and leakage for edge caching have not been fully addressed in existing literature. This paper proposes a Tangle based caching model for edge services, as well as an efficient and effective scheme that will aid the offloading process between IoT devices and edge devices. The proposed model utilizes reversible integer transformation in the context of edge computing to build a secure data transfer technique that distorts the original data so that an adversary cannot access it. Experimental studies and theoretical evaluation demonstrate that our proposed scheme is promising for adoption in future IoT systems with its efficiency and security features.

Following the globalization initiated by containerization of logistics, supply chains might be due another revolution by the integration of the disruptive blockchain technology that addresses the current issues with the management of complex global supply chains. Blockchains are distributed digital ledgers that require no central authority to operate while offering a tamper-proof and transparent history of each transaction from the very beginning. Distributed nature of these ledgers ensure that every participant of the supply chain has access to trusted data. The industry has already begun experimenting with blockchain integration into their operations. For the majority of the organizations, however, these experiments stay in proof-of-concept stages or small pilot studies. In this chapter, the authors discuss the supply chain characteristics that make blockchain integration favorable, lay the groundwork for how blockchain can be used for supply chain operations and how it has been used so far.

Bilingual or even polylingual word embeddings created many possibilities for tasks involving multiple languages. While some tasks like cross-lingual information retrieval aim to satisfy users’ multilingual information needs, some enable transferring valuable information from resource-rich languages to resource-poor ones. In any case, it is important to build and evaluate methods that operate in a cross-lingual setting. In this paper, Wordnet definitions in 7 different languages are used to create a semantic textual similarity testbed to evaluate cross-lingual textual semantic similarity methods. A document alignment task is created to be used between Wordnet glosses of synsets in 7 different languages. Unsupervised textual similarity methods—Wasserstein distance, Sinkhorn distance and cosine similarity—are compared with a supervised Siamese deep learning model. The task is modeled both as a retrieval task and an alignment task to investigate the hubness of the semantic similarity functions. Our findings indicate that considering the problem as a retrieval and alignment problem has a detrimental effect on the results. Furthermore, we show that cross-lingual textual semantic similarity can be used as an automated Wordnet construction method.