Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made possible by higher integration of physical and digital elements. In today's pervasive cyber-physical systems, connecting more devices introduces new vulnerabilities and security threats. With increasing cybersecurity incidents, cybersecurity professionals are becoming incapable of addressing what has become the greatest threat climate than ever before. This research investigates the spectrum of risk of a cybersecurity incident taking place in the cyber-physical-enabled world using the VERIS Community Database. The findings were that the majority of known actors were from the US and Russia, most victims were from western states and geographic origin tended to reflect global affairs. The most commonly targeted asset was information, with the majority of attack modes relying on privilege abuse. The key feature observed was extensive internal security breaches, most often a result of human error. This tends to show that access in any form appears to be the source of vulnerability rather than incident specifics due to a fundamental trade-off between usability and security in the design of computer systems. This provides fundamental evidence of the need for a major reevaluation of the founding principles in cybersecurity.

Summary In recent years, massive growth in the number of images on the web has raised the requirement of developing an effective indexing model to search digital images from a large‐scale database. Though cloud service offers effective indexing of compressed images, it remains a major issue due to the semantic gap between the user query and diverse semantics of large‐scale database. This article presents a radix trie indexing (RTI) model based on semantic visual indexing for retrieving the images from cloud platforms. Initially, an interactive optimization model is applied to identify the joint semantic and visual descriptor space. Next, an RTI model is applied to integrate the semantic visual joint space model for finding an effective solution for searching large‐scale sized dataset. Finally, a Spark distributed model is applied for deploying the online image retrieval service. The performance of the proposed method is validated on two standard dataset, namely, Holidays 1 M and Oxford 5 K in terms of mean average precision (mAP) and processing time under varying dataset sizes. During experimentation, the presented RTI model shows the maximum mAP value of 0.83 under the dataset size of 1000. Similarly, under the sample count of 1000, it is noted that the standalone server requires a maximum of 118 minutes to complete the process, whereas the spark cluster requires a minimum of around only 19 minutes to finish the process. The experimental outcome showed improvement in terms of various measures over the best rivals in the literature.

Abstract-Clouds are distributed Internet-based platforms that provide highly resilient and scalable environments to be used by enterprises in a multitude of ways. Cloud computing offers enterprises technology innovation that business leaders and IT infrastructure managers can choose to apply based on how and to what extent it helps them fulfil their business requirements. It is crucial that all technical consultants have a rigorous understanding of the ramifications of cloud computing as its influence is likely to spread the complete IT landscape. Security is one of the major concerns that is of practical interest to decision makers when they are making critical strategic operational decisions. Distributed Denial of Service (DDoS) attacks are becoming more frequent and effective over the past few years, since the widely publicised DDoS attacks on the financial services industry that came to light in September and October 2012 and resurfaced in the past two years. In this paper, we introduce advanced cloud security technologies and practices as a series of concepts and technology architectures, from an industry-centric point of view. This is followed by classification of intrusion detection and prevention mechanisms that can be part of an overall strategy to help understand identify and mitigate potential DDoS attacks on business networks. The paper establishes solid coverage of security issues related to DDoS and virtualisation with a focus on structure, clarity, and well-defined blocks for mainstream cloud computing security solutions and platforms. In doing so, we aim to provide industry technologists, who may not be necessarily cloud or security experts, with an effective tool to help them understand the security implications associated with cloud adoption in their transition towards more knowledge-based systems.

Wireless sensor network is a hot research topic with massive applications in different domains. Generally, wireless sensor network comprises hundreds to thousands of sensor nodes, which communicate with one another by the use of radio signals. Some of the challenges exist in the design of wireless sensor network are restricted computation power, storage, battery and transmission bandwidth. To resolve these issues, clustering and routing processes have been presented. Clustering and routing processes are considered as an optimization problem in wireless sensor network which can be resolved by the use of swarm intelligence–based approaches. This article presents a novel swarm intelligence–based clustering and multihop routing protocol for wireless sensor network. Initially, improved particle swarm optimization technique is applied for choosing the cluster heads and organizes the clusters proficiently. Then, the grey wolf optimization algorithm–based routing process takes place to select the optimal paths in the network. The presented improved particle swarm optimization–grey wolf optimization approach incorporates the benefits of both the clustering and routing processes which leads to maximum energy efficiency and network lifetime. The proposed model is simulated under an extension set of experimentation, and the results are validated under several measures. The obtained experimental outcome demonstrated the superior characteristics of the improved particle swarm optimization–grey wolf optimization technique under all the test cases.

This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service.