Academic Articles & Research Papers

Addressing Industry 4.0 Cybersecurity Challenges

Awareness is building on how managers should be involved in Industry 4.0 cybersecurity. This awareness and concern derives from heavy dependence of integrated information systems and technology for Industry 4.0. Manufacturing companies rely...

The Role of Machine Learning in Cybersecurity

Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a s...

Cybersecurity for Industry 4.0 in the current literature: A reference framework

Blockchain for Cybersecurity in Smart Grid: A Comprehensive Survey

Blockchain is an immutable type of distributed ledger that is capable of storing data without relying on a third party. Blockchain technology has attracted significant interest in research areas, including its application in the smart grid ...

Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review

Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained increasing interest from both researchers and companies. This has caused a rapid expansion of scientific production on DL-AV in recent years, encouraging res...

Experiential Learning: Innovative Approaches to Post-Secondary Cybersecurity Education

The cybersecurity profession continues to face a significant shortfall of qualified professionals despite steady growth in degree programs. Employers consistently cite experience as the main barrier for entry-level cybersecurity hires. This...

European Ai-Policy Space

Artificial Intelligence (AI) is one of the central technologies of the fourth industrial revolution. A significant part of the production of surplus value in the XXI century is associated with the mastery and widespread introduction of tech...

Feddsg: backdoor defense via semantic filter and geometric constraint in federated learning

Abstract Backdoor attacks pose a serious threat to Internet-of-Things (IoT) federated learning. In IoT deployments, pronounced non-independent and identically distributed (non-IID) data heterogeneity causes benign client updates to exhibit ...

A Review of the Insider Threat, a Practitioner Perspective Within the U.K. Financial Services

The insider threat within organisational cybersecurity continues to be of great concern globally. The current insider threat detection strategies are acknowledged as ineffective, evidenced by the increased reported events in high-profile in...

The impact of corporate social responsibility on cybersecurity behavior: The crucial role of organizationally-prescribed perfectionism

Abstract This study delves into the ways in which corporate social responsibility (CSR) affects cybersecurity behavior among employees. It specifically examines the mediating effects of psychological safety and job stress, as well as the mo...

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the emerging Internet of Things (IoT) has gained a lot of attention from the government, academia, and industries in recent years. Federated cybersecurity (F...

China and Cybersecurity

China's emergence as a great power in the twenty-first century is strongly enabled by cyberspace. Leveraged information technology integrates Chinese firms into the global economy, modernizes infrastructure, and increases internet penetrati...

Cybersecurity Risks in a Pandemic

Cybersecurity threats are estimated to cost the world US $6 trillion a year by 2021, and the number of attacks has increased five-fold after COVID-19. Although there is substantial literature on the threats technological vulnerabilities hav...

Automated monitoring innovations for efficient and safe construction practices

As construction projects increase in complexity, there are growing challenges with conventional monitoring methods in terms of efficiency, safety, and competitiveness. Traditional supervision techniques are labour-intensive, intermittent, a...

Decentralized intelligence in smart grids: a comprehensive survey of edge computing and federated learning

Modern power grids are becoming more distributed and data-intensive, while centralized, cloud-based analytics struggle to keep pace. Routing everything through a central system introduces delays, strains network capacity, complicates compli...

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system

The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic ...

Practical Vulnerability-Information-Sharing Architecture for Automotive Security-Risk Analysis

Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanica...

Security evaluation framework for cloud ERP systems using NIST and ISO standards

Abstract Enterprise Resource Planning (ERP) systems serve as critical infrastructure for modern organizations, yet their security assessment lacks standardized evaluation frameworks. This study develops and applies a structured Security Mat...

A Cooperative Learning Approach for Decentralized Peer-to-Peer Energy Trading Markets and its Structural Robustness Against Cyberattacks

Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different...

Cybersecurity for Industry 4.0