Data volume, velocity, and structure have significantly evolved over the years. The complex networking architectures of current infrastructures, and the development, and accessibility of cloud services to a diverse user base have introduced numerous challenges which have raised concerns regarding the quality-of-service performance in data processing for both service providers and customers. Key issues identified in the context of big data transfer services for cloud data centers include storage, big data transfer, service transfer architecture, data processing, bandwidth, and security, all of which demand extensive research. After thoroughly screening selected peer-reviewed articles, the primary open issues are: incorporating a data placement module in the data transfer service, providing end-to-end safeguards for packet delivery, improving data transfer time and speed, minimizing costs and implementation overhead, ensuring secure data transfer between servers in the cloud, demonstrating effective big data transfer architectures, considering topology-specific extensions to reduce the busty nature of data centers, and enhancing data transfer services using machine learning algorithms during upload and download operations. This article presents a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to understand current trends, methods/models, and research problems on big data transfer services for cloud data centers. Future work considers the security and privacy of big data transmission in cloud environments.

Collaborative Intrusion Detection System (CIDS) protect large networks against distributed attacks. However, a CIDS is vulnerable to insider attacks that decrease the mutual trust among the CIDS nodes. Most existing trust management approaches rely on a central authority, trusted third parties or network peers for managing trust. The current techniques are prone to high false positives and vulnerable to various reputation attacks. For instance, device attestation manages trust among CIDS nodes by verifying the integrity of a node’s hardware and software configuration. However, it lacks real-time monitoring of the dynamic state, limiting its effectiveness against ongoing attacks and malware. Therefore, incorporating the system’s dynamic state in the trust framework is crucial, but it causes false positives requiring corrective mechanisms. To address these challenges, this paper proposes a blockchain-based integrated trust management framework for CIDS, incorporating the device’s genome attestation, the system’s dynamic parameters, and a false positive resilient reputation mechanism. By storing the reputation scores on the blockchain, the framework alleviates the need for a third party for trust management and thus mitigates attacks applicable to reputation-based systems. The paper performs a comprehensive security and performance analysis of the proposed framework to gauge its efficiency and study the effects of a penalty on a node’s reputation during the recovery and rally phases. We also study the impact of false positives on the reputation of a node. The results show that Hyperledger Fabric offers lower transaction latency and low CPU utilization compared to Ethereum Blockchain.

The Internet of Things (IoT) plays a significant role in our daily lives as interconnection and automation positively impact our societal needs. In contrast to traditional devices, IoT devices require connectivity and data sharing to operate effectively. This interaction necessitates that data resides on multiple platforms and often across different locations, posing challenges from a digital forensic investigator's perspective. Recovering a full trail of data requires piecing together elements from various devices and locations. IoT-based forensic investigations include an increasing quantity of objects of forensic interest, the uncertainty of device relevance in terms of digital artifacts or potential data, blurry network boundaries, and edgeless networks, each of which poses new challenges for the identification of significant forensic artifacts. One example of the positive societal impact of IoT devices is that of Humanoid robots, with applications in public spaces such as assisted living, medical facilities, and airports. These robots use IoT to provide varying functionality but rely heavily on supervised learning to customize their utilization of the IoT to various environments. A humanoid robot can be a rich source of sensitive data about individuals and environments, and this data may assist in digital investigations, delivering additional information during a crime investigation. In this paper, we present our case study on the Zenbo Humanoid Robot, exploring how Zenbo could be a witness to a crime. In our experiments, a forensic examination was conducted on the robot to locate all useful evidence from multiple locations, including root-level directories using logical acquisition.