In recent years, the frequent leakage of sensitive information in natural language texts has presented significant challenges in text distribution, sharing, and migration. Existing methods for text protection and watermarking are limited, capable of either hide sensitive data or prevent unauthorized copying and tampering, but not both simultaneously. To address these limitations, this paper proposes a masking-watermarking cooperative framework designed to hide text-sensitive information, prevent unintentional data leakage, and ensure content ownership verification and tampering prevention. The framework introduces three novel techniques: a variable autoencoder to ensure diverse watermark generation, an improved transformer to enhance the adaptability of dynamic masks, and a dual discriminator for joint verification of text and watermarks. A comprehensive evaluation was conducted, covering text similarity, masking flexibility, and the imperceptibility of text masking, as well as watermark classification recognition and robustness against various attacks. The proposed framework achieved a score of 0.98 on the SBERT metric, demonstrating its effectiveness in achieving imperceptible text masking and robust watermark embedding.

Due to its nature of dynamic, mobility, and wireless data transfer, the Internet of Vehicles (IoV) is susceptible to a wide range of cyber threats, including spoofing, Distributed Denial of Service (DDoS) attacks, and malware. intrusion detection systems (IDS) play a vital role in protecting the IoV ecosystem by continuously monitoring network traffic to detect and respond to intrusions, malicious activities, and policy violations in real time. However, most existing research has focused on centralized, machine learning (ML)-based IDS solutions for IoV, often overlooking its inherently distributed architecture. Due to their high computational demands, these centralized systems often depend on Cloud resources to detect cyber threats, which can lead to increased response delays. On the other hand, Edge nodes typically lack the necessary resources to train and deploy complex ML and deep learning algorithms. To address this issue, this article proposes an effective hierarchical classification framework designed for IoV networks. Hierarchical classification enables classifiers to be trained and deployed across multiple levels. This allows Edge nodes to independently identify specific types of attacks. With this approach, Edge nodes can conduct targeted attack detection while utilizing Cloud nodes for more comprehensive threat analysis and coordination. Considering the resource limitations of Edge nodes, we employ the Boruta feature selection method to reduce data dimensionality and enhance processing efficiency. To evaluate our proposed framework, we utilize the latest IoV security dataset CIC-IoV2024 and CIC-DDoS2019 datasets, achieving promising results that demonstrate the feasibility and effectiveness of our models in securing IoV networks. This hierarchical framework might improve the scalability and responsiveness of intrusion detection in distributed IoV environments. By offloading lightweight detection tasks to Edge nodes and reserving deeper analysis for the Cloud, the model can reduce latency and network load, making real-time threat response more feasible. The proposed approach can offer a practical solution for deploying effective, resource-aware cybersecurity mechanisms in real-world vehicular networks, where traditional centralized systems fall short.

The rise of mobile devices and the Internet of Things has generated vast data which require efficient processing methods. Volunteer Computing (VC) is a distributed network that utilises idle resources from diverse devices for task completion. VC offers a cost-effective and scalable solution for computation resources. Mobile Volunteer Computing (MVC) capitalises on the abundance of mobile devices as participants. However, managing a large number of participants in the network presents a challenge in scheduling resources. Various resource allocation algorithms and MVC platforms have been developed, but there is a lack of survey papers summarising these systems and algorithms. This paper aims to bridge the gap by delivering a comprehensive survey of MVC, including related technologies, MVC architecture, and major finding in taxonomy of resource allocation in MVC.

Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user’s requirements in terms of data processing and deadline for decision making. In this article, we propose two multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments.

SLA violations do happen in real world. An SLA violation represents the failure of guaranteeing a service, which leads to unwanted consequences such as penalty payments, profit margin reduction, reputation degradation, customer churn and service interruptions. Hence, in the context of cloud-hosted big data analytics applications (BDAAs), it is paramount for providers to predict and prevent SLA violations. While machine learning-based techniques have been applied to detect SLA violations for web service or general cloud service, the study on detecting SLA violations dedicated for cloud-hosted BDAAs is still lacking. In this article, we propose four machine learning techniques and integrate 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud. We evaluate the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba). Our work not only helps providers to choose the best performing prediction technique, but also provides them capabilities to uncover the hidden pattern of multiple configurations of BDAAs across layers.

Many IoT applications and services such as smart parking and smart traffic control contain a network of different analytical components, which are composed in the form of a workflow to make better decisions. These workflows are also known as stream workflows. The focus of existing research works is on the streaming operator graph, which differs from stream workflow application as it involves heterogeneity, multiple data sources and multiple outputs. Considering the complexity and dynamism of stream workflow, meeting real-time data analysis requirements at deployment time is not the whole story as the velocity of data changes over time. This change is the most dynamic form of stream workflow that occurs frequently during the execution of this application. In this article, we propose a new dynamic scheduling technique that manages cloud resources over time to handle data velocity changes in stream workflow while maintaining user-defined real-time data analysis requirements and minimising execution cost. The efficiency of the proposed technique is evaluated, and experimental results showed that this technique outperformed its competitors and is close to the lower bound.