Cybersecurity Awareness and Training (CAT) Framework for Remote Working Employees

Currently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations’ critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The COVID-19 pandemic has generated different cybersecurity issues and challenges for businesses as employees have become accustomed to working from home. Firms are speeding up their digital transformation, making cybersecurity the current main concern. For software and hardware systems protection, organizations tend to spend an excessive amount of money procuring intrusion detection systems, antivirus software, antispyware software, and encryption mechanisms. However, these solutions are not enough, and organizations continue to suffer security risks due to the escalating list of security vulnerabilities during the COVID-19 pandemic. There is a thriving need to provide a cybersecurity awareness and training framework for remote working employees. The main objective of this research is to propose a CAT framework for cybersecurity awareness and training that will help organizations to evaluate and measure their employees’ capability in the cybersecurity domain. The proposed CAT framework will assist different organizations in effectively and efficiently managing security-related issues and challenges to protect their assets and critical information. The developed CAT framework consists of three key levels and twenty-five core practices. Case studies are conducted to evaluate the usefulness of the CAT framework in cybersecurity-based organizational settings in a real-world environment. The case studies’ results showed that the proposed CAT framework can identify employees’ capability levels and help train them to effectively overcome the cybersecurity issues and challenges faced by the organizations

Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems

[EN] Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, we propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. We effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on our newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of our approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making our system more suitable for IoT-driven fire disaster management in ITSs.Muhammad, K.; Ullah, H.; Khan, S.; Hijji, M.; Lloret, J. (2023). Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 24(11):13141-13150. https://doi.org/10.1109/TITS.2022.32038681314113150241

Towards the Development of a Capability Assessment System for Flood Risk Management

Having in place adequate levels of emergency management capabilities (EMCs) underpins a managed civil emergency response, especially during a flooding event(s). Good EMC is either built on having the right internal capabilities or by exploiting existing emergency capabilities from other responders. In some countries, such as Saudi Arabia, there is a noted lack of decision‐making in the Civil Defence (CD) Authority about generating effective mutual‐aid requests. Three core areas of EMC include having the right types and levels of response equipment to hand, ensuring sufficient Human Resources, can be maintained throughout a sustained event, and developing adequate Training capabilities. Other factors impacting on Saudi Arabia include both stress and a lack of work experience. In this chapter, we examine the effectiveness of a prototype IT System in the case of Saudi CD Authority as a tool for addressing the availability and adequacy of mutual‐aid for EMC, Human Resources (HR), and training capabilities against scalable levels of flood risk event(s). The proposed IT System is built using the ‘fuzzy expert system’ approach

Mapping Tools for Open Source Intelligence with Cyber Kill Chain for Adversarial Aware Security

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Cloud Servers: Resource Optimization Using Different Energy Saving Techniques

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique

Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e. achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain.This work was supported by the European Commission through European Union (EU) and Japan for Artificial Intelligence (AI) under Grant 957339

6G Connected Vehicle Framework to Support Intelligent Road Maintenance using Deep Learning Data Fusion

The growth of IoT, edge and mobile Artificial Intelligence (AI) is supporting urban authorities exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV), to drive the development of transformative intelligent transport applications for addressing smart city challenges. A critical challenge is timely and efficient road infrastructure maintenance. This paper proposes an intelligent hierarchical framework for road infrastructure maintenance that exploits the latest developments in 6G communication technologies, deep learning techniques, and mobile edge AI training approaches. The proposed framework abides with the stringent requirements of training efficient machine learning applications for CAV, and is able to exploit the vast numbers of CAVs forecasted to be present on future road networks. At the core of our framework is a novel Convolution Neural Networks (CNN) model which fuses imagery and sensory data to perform pothole detection. Experiments show the proposed model can achieve state of the art performance in comparison to existing approaches while being simple, cost- effective and computationally efficient to deploy. The proposed system can form part of a federated learning framework for facilitating large scale real-time road surface condition monitoring and support adaptive resource allocation for road infrastructure maintenance

Blockchain-based secure and intelligent data dissemination framework for UAVs in battlefield applications

The modern warfare scenario has immense challenges that can risk personnel's lives, highlighting the need for data acquisition to win a military operation successfully. In this context, unmanned aerial vehicles (UAVs) play a significant role by covertly acquiring reconnaissance data from an enemy location to make the friendly troops aware. The acquired data is mission-critical and needs to be secured from the intruders, which can implicitly manipulate it for their benefit. Moreover, UAVs collect a large amount of data, including high-definition images and surveillance videos; handling such a massive amount of data is a bottleneck on traditional communication networks. To mitigate these issues, this article proposes a blockchain and machine learning (ML)-based secure and intelligent UAV communication underlying sixth-generation (6G) networks, that is, Block-USB. The proposed system refrain the disclosure of highly-sensitive military operations from intruders (either a rogue UAV or a malicious controller). The proposed system uses off-chain storage, that is, Interplanetary file system (IPFS), to improve the blockchain storage capacity. We also present a case study on securing UAV-based military operations by considering multiple scenarios considering controller/UAV malicious. The performance of the proposed system outperforms the traditional baseline 4G/5G and non IPFS-based systems in terms of classification accuracy, communication latency, and data scalability

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance

The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, especially in uncertain sports surveillance situations. To this end, we present a framework for deciding the winner in a tied sporting event. As a case study, a tied cricket match was investigated, and the issue was addressed with a systematic state-of-the-art approach by considering the team strength in terms of the player score, team score at different intervals, and total team scores (TTSs). The TTSs of teams were compared to recommend the winner. We believe that the proposed idea will help to identify the winner in a tied match, supporting intelligent surveillance systems. In addition, this approach can potentially address many existing issues and future challenges regarding critical decision-making processes in sports. Furthermore, we posit that this work will open new avenues for researchers in fractal AI