Cybersecurity Threats Research Articles

Self-Rectifying Memristor-Based Reservoir Computing for Real-Time Intrusion Detection in Cybersecurity.

The increasing sophistication of cybersecurity threats, driven by the proliferation of big data and the Internet of Things (IoT), necessitates the development of advanced real-time intrusion detection systems (IDSs). In this study, we present a novel approach that integrates NiO-doped WO3-x/ZnO bilayer self-rectifying memristors (SRMs) within a reservoir computing (RC) framework for IDS applications. The proposed crossbar array architecture exploits the exceptional dynamic properties of SRMs, achieving a classification accuracy of 93.07% on the CSE-CIC-IDS2018 data set, while demonstrating ultrahigh information-processing efficiency. Our approach not only leverages the tunable characteristics of memristors but also addresses the challenge of sneak path currents in large-scale integration, offering a robust and scalable solution for next-generation IDS. This work exemplifies the power of emerging electronics in enhancing cybersecurity through innovative hardware implementations.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Real-Time Data Governance and Compliance in Cloud-Native Robotics Systems

This study investigates the frameworks and challenges of real-time data governance and compliance in cloud-native robotics systems, focusing on data integrity, cloud security, regulatory adherence, and cybersecurity risks. Using extensive datasets from the Amazon AWS Open Data Registry, the EU GDPR Enforcement Tracker, and Kaggle’s IoT dataset, the analysis explores cloud-native systems' data accuracy, security, and governance. Data were extracted through a standardized process: performance metrics, including latency and error rates, were gathered from Amazon AWS to assess system efficiency, GDPR violation records were analyzed from the EU Enforcement Tracker to understand compliance trends, and data volume and governance metrics from Kaggle’s IoT dataset were correlated to identify governance challenges. Together, these data sources provide comprehensive insights into how cloud-native systems can be optimized for real-time operations. The study highlights the cloud security benefits and governance advantages inherent to cloud-native frameworks, such as real-time monitoring, automated threat detection, and data encryption, which collectively reduce unauthorized access risks while supporting operational efficiency. Findings indicate high data accuracy (0.51% error rate) and low latency (mean of 48.96 ms) across systems; however, processing time variability (standard deviation of 28.61 ms) signals a need for further optimization in time-sensitive environments. The regression analysis of GDPR violations reveals a substantial penalty increase of €53,789.41 per violation, emphasizing the financial risks of non-compliance. Correlation analysis (r = 0.083 for data volume and governance failures) suggests that external cybersecurity threats have a greater impact on governance than internal metrics, underscoring the importance of adaptive governance frameworks that support both data integrity and regulatory compliance in cloud-native robotics systems.

Assessing Employee Susceptibility to Cybersecurity Risks

This research challenges assumptions about cybersecurity risk factors, revealing that age, gender, and educational background are not significant determinants of employee susceptibility. It highlights the importance of inclusive cybersecurity training programs that cater to individuals of all age groups, dispelling the misconception that older employees are inherently less tech-savvy and more susceptible to cybersecurity threats. The findings show that cybersecurity teams within organizations significantly impact the adoption of security policies and data handling practices among employees, even though their influence on password and account security practices is limited. Organizations can adopt a holistic approach to cybersecurity training and awareness programs by leveraging these insights. This approach transcends traditional demographics and focuses on enhancing password and account security, ultimately strengthening cybersecurity postures, fostering a culture of cybersecurity consciousness, and fortifying defenses against the evolving landscape of digital threats.

Innovating Building Refurbishments: Embracing Global Digital Transformation for Today's Construction Industry

The Uberization of the construction industry is a clear indication that the sector is gradually shifting towards the digital era. This transformation signifies a significant digitization of construction, incorporating smart technology in various phases, from design and planning to execution, operations, and management. This paper delves into the importance of embracing digital technologies in building refurbishments to meet the evolving demands of today's construction landscape. By integrating innovative digital solutions such as Building Information Modelling (BIM), Internet of Things (IoT), artificial intelligence, robotics, and other cutting-edge technologies, refurbishment projects can achieve cost-effectiveness, precision, and environmental sustainability. The benefits of this digital shift encompass improved project coordination, safety, cost efficiency, and environmental impact management. However, challenges like high initial costs, resistance to change, cybersecurity threats, and skill shortages persist. The digital transformation is also shaped by global trends such as urbanization, population growth, and the push for smart, sustainable cities. Governments and private sectors acknowledge the potential of digital construction in meeting these demands more effectively. Thus, the industry must adapt to these technological advancements to stay competitive and address future infrastructure needs. The primary goal of this paper is to underscore the importance of embracing global digital transformation in building refurbishments, offering insights and recommendations for stakeholders in the construction sector to leverage digital technologies effectively for enhanced project outcomes and long-term sustainability. This study aims to explore the global digitalization of building construction, examining how advanced technologies like Building Information Modelling (BIM), Artificial Intelligence (AI), and the Internet of Things (IoT) are revolutionizing construction processes. By identifying the main drivers of digitalization in the construction industry, assessing the role of emerging technologies, and evaluating the benefits such as enhanced efficiency, cost savings, improved safety, and sustainability, this paper aims to showcase the necessity of digitalization to meet modern demands effectively. These abstract outlines the key concepts discussed in the paper, highlighting the advantages of integrating digital tools in building refurbishments, the obstacles, and opportunities in digital transformation within construction, and the potential impact on sustainability and project outcomes. Through case studies and industry insights, the study will demonstrate how digitalization is vital for staying abreast of modern requirements. Additionally, it will analysis case studies and successful implementations of digital technologies in refurbishment projects to illustrate best practices and lessons learned.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Enhancing Cybersecurity Resilience through Advanced Red Teaming Exercises and MITRE ATT&CK Framework Integration: A Paradigm Shift in Cybersecurity Assessment

As cybersecurity threats evolve at an alarming pace, conventional defense strategies are becoming progressively less effective. Addressing this urgent challenge, our study introduces a transformative approach to red teaming exercises by integrating the MITRE ATT&CK framework. This novel integration utilizes real-world attacker tactics and behaviors to create highly realistic scenarios that rigorously test defenses and reveal previously unidentified vulnerabilities. Our comprehensive evaluation confirms a significant enhancement in the realism and effectiveness of red teaming, leading to improved vulnerability identification and the generation of actionable insights for proactive remediation. This study uniquely contributes by providing a structured, data-driven methodology that not only aligns current defenses with emergent threat tactics but also promotes continuous improvement in cybersecurity resilience. Such advancements are crucial for organizations aiming to stay ahead of attackers and maintain robust cyber defenses in dynamic threat environments. The significance of this study lies in providing a structured approach to bolster cybersecurity resilience against evolving threats and offering a model for continuous defense improvement. Future research should explore the impact of such integration on long-term cybersecurity strategies.

DeepSecure: A computational design science approach for interpretable threat hunting in cybersecurity decision making

Businesses and industries are placing a greater emphasis on information systems for cybersecurity decision-making due to the rising cybersecurity threat landscape and the critical need to protect their digital assets. Threat hunting provides a data-driven and proactive approach to cybersecurity, enabling organizations to efficiently detect, analyze, and respond to cyber threats in real-time. Despite playing a crucial role, these systems face several obstacles, including the manual analysis of technical threat intelligence, the non-Gaussian nature of real-world data, the high rate of false positives produced during threat hunting, and the lack of interpretation and justification for these complex models. This article adopts the computational design science paradigm to develop a novel IT artifact for threat-hunting named DeepSecure. First, to automatically extract latent patterns from multivariate time series datasets, we propose a dynamic vector quantized variational autoencoder technique. Second, a multiscale hierarchical attention bi-directional gated recurrent unit-based threat-hunting mechanism is designed. Finally, we provide the visualization of attention scores to aid in model interpretation. We evaluate the DeepSecure against state-of-the-art benchmarks on two publicly available datasets, namely, ToN-IoT and CSE-CIC-IDS2018. The experimental evaluation proves that our model can efficiently identify threat types. Beyond demonstrating practical utility, the proposed framework can help address the lack of interpretation and justification for complex models in cyber threat detection and will allow organizations to respond to potential security incidents quickly.

Keamanan Siber dalam Era Digital: Tantangan dan Solusi

Cybersecurity is becoming a major challenge in today's digital era along with the increasing reliance on information and communication technology. This paper explores various cybersecurity threats such as DDoS attacks, malware, and ransomware and the solutions that can be applied, including the implementation of Vulnerability Assessment and Penetration Testing (VAPT) using tools such as Nmap and Nikto. This research aims to provide insight into the best techniques in cyber threat mitigation and discuss the relevance of VAPT practices in maintaining system integrity.

Generative AI for Vulnerability Management: A Blueprint

Cybersecurity threats continue to evolve in complexity and scale every year. The need for more effective vulnerability management has become more important now than ever.

Advanced Detection Techniques to neutralize Supply Chain Cyber-attacks on small and medium businesses in the United States: The Machine Learning and AI Approach.

Cyber-attacks targeting supply chains represent a significant and increasing risk for small and medium-sized businesses (SMBs) in the United States. This paper examines the role of machine learning (ML) and artificial intelligence (AI) in detecting and mitigating these cybersecurity threats. Key challenges faced by SMBs include high implementation costs, a shortage of skilled personnel, inadequate data quality, and the complexity of integrating advanced technologies into existing systems. Despite these hurdles, the paper identifies strategic approaches that can empower SMBs to effectively leverage ML and AI, including affordable solutions, targeted training, and improved data management practices. It emphasizes the importance of addressing privacy and usability concerns to facilitate successful technology adoption. Recommendations include pursuing cost-effective, subscription-based AI and ML models, utilizing government incentives, and enhancing workforce skills through training and partnerships. Additionally, collaboration with larger organizations can improve data management and system efficacy. By implementing robust protective measures and increasing awareness through educational initiatives, SMBs can strengthen their cybersecurity posture and better navigate the evolving landscape of cyber threats.

Digitalisation and Cybersecurity: Towards an Operational Framework

As businesses increasingly adopt digital processes and solutions to enhance efficiency and productivity, they face heightened cybersecurity threats. Through a systematic literature review and concept development, this article examines the intersection of digitalisation and cybersecurity. It identifies the methodologies and tools used for cybersecurity assessments, factors influencing the adoption of cybersecurity measures, and the critical success factors for implementing these measures. The article also puts forward the concept of cybersecurity governance process categories, which are used to classify the factors uncovered in the research. Findings suggest that current information security standards tend to be too broad and not adequately tailored to the specific needs of small and medium-sized enterprises (SMEs) when implementing emerging technologies, like Internet of Things (IoT), blockchain, and artificial intelligence (AI). Additionally, these standards often employ a top-down approach, which makes it challenging for SMEs to effectively implement them, as they require more scalable solutions tailored to their specific risks and limited resources. The study thus proposes a new framework based on the Plan-Do-Check model, built around the cybersecurity governance process categories and the three core pillars of governance, culture and standards. This is essentially a bottom-up approach that complements current top-down methods, and will be of value to both information technology (IT) professionals as an operational guide, and to researchers as a basis for future research in this field.

Cybersecurity threats and preparedness: Implications for dental schools.

Cybersecurity threats are a growing concern in healthcare, where digital systems now underpin patient care, financial management, and educational operations. A cybersecurity breach in a Dental school environment can have widespread consequences to the mission of the school-patient care, research, education and service. For dental school administrators, these risks highlight the necessity of robust cybersecurity measures. For student learners, the impact may include interruptions to their education. For patients, it could mean compromised personal data and reduced access to clinical care. While many sectors have responded to increasing cyber threats by enhancing their defenses, healthcare and dental schools, often lag in implementing necessary protections. This emphasizes the need for proactive measures, such as regular system audits, advanced encryption methods, and ongoing cybersecurity training for administrators and students alike, to mitigate future risks and safeguard institutional integrity.

From Monitoring to Observability: A Paradigm Shift in IT Operations

This article explores the paradigm shift from traditional monitoring to observability in IT operations, driven by the increasing complexity of modern distributed systems, data overload, evolving cybersecurity threats, and the need for proactive problem-solving. Observability platforms, built on the four pillars of events, metrics, traces, and logs, provide comprehensive insights into system behavior and performance. Adopting observability practices offers numerous benefits, including automated discovery of system components, dependency mapping, topology visualization, correlative intelligence, faster root cause diagnosis, and more accurate anomaly detection through auto-baselining. Organizations that embrace observability report significant improvements in incident response times, system reliability, and overall operational efficiency. The article guides successfully transitioning to an observability-focused approach by embracing automation, fostering a culture of observability, implementing the right tools, and leveraging AI and machine learning for advanced analytics and insights.

Modeling Cybersecurity Risk: The Integration of Decision Theory and Pivot Pairwise Relative Criteria Importance Assessment with Scale for Cybersecurity Threat Evaluation

This paper presents a comprehensive model for cyber security risk assessment using the PIPRECIA-S method within decision theory, which enables organizations to systematically identify, assess and prioritize key cyber threats. The study focuses on the evaluation of malware, ransomware, phishing and DDoS attacks, using criteria such as severity of impact, financial losses, ease of detection and prevention, impact on reputation and system recovery. This approach facilitates decision making, as it enables the flexible adaptation of the risk assessment to the specific needs of an organization. The PIPRECIA-S model has proven to be useful for identifying the most critical threats, with a special emphasis on ransomware and DDoS attacks, which represent the most significant risks to businesses. This model provides a framework for making informed and strategic decisions to reduce risk and strengthen cyber security, which are critical in a digital environment where threats become more and more sophisticated.

A Study On Present Status of E Banking Services in Tamil Nadu

E-banking has rapidly transformed the banking landscape in Tamil Nadu, driven by technological advancements and increasing internet penetration. This paper examines the current status of e-banking services in the state, highlighting key features such as mobile banking, online transactions, and digital wallets. The rise of fintech companies has introduced innovative solutions, enhancing customer experience and accessibility. Despite the growth, challenges remain, including cybersecurity threats and digital literacy gaps among certain demographics. The government's initiatives, such as the Digital India campaign, aim to promote e-banking and financial inclusion. Overall, Tamil Nadu is witnessing a robust adoption of e-banking, making it a crucial player in India's digital financial ecosystem.

The Impact and Challenges of Digital Transformation on Supply Chain Resilience in Physical Enterprises

Abstract: In the digital era, supply chain resilience (SCR) has become a key pillar of the real economy. Enterprises face various challenges, including cybersecurity threats, market volatility, technological change and changing global conditions. A strong SCR not only ensures continued business operations but also maintains market stability and consumer well-being. Digital transformation helps businesses anticipate market changes, optimize inventory management, improve operational efficiency, and assist in management decisions when necessary by providing unprecedented data transparency and real-time insights. The purpose of this paper is to examine the extent of digital transformation in the supply chains of physical firms globally through a literature review, to identify the factors influencing the resilience of digital transformation in the supply chains of physical firms, and to discover the barriers to digital transformation in the supply chains of physical firms. The study finds that the digital transformation of physical firms' supply chains is still in its infancy, and there are barriers at both managerial and physical levels. Solutions to address these barriers include enhancing employee training, correctly introducing appropriate digital technologies, and improving information security.

Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions

The maritime industry is increasingly confronted with a myriad of cybersecurity challenges exacerbated by extreme environmental conditions, technological advancements, and heightened reliance on automation. This review paper discusses the intersection of these factors, focusing on the adoption of artificial intelligence (AI)-based intrusion detection systems (IDS) and network automation as vital strategies for mitigating cybersecurity risks. The paper begins by outlining the unique cybersecurity threats faced by the maritime sector, which include data breaches, phishing attacks, and malware threats, all amplified by adverse weather and geographical isolation. In light of these challenges, the rationale for integrating AI-driven solutions into maritime operations is discussed. AI-based IDS can enhance threat detection capabilities through advanced machine learning algorithms that adapt to evolving cyber threats while minimizing false positives. Additionally, network automation can improve connectivity and data security, facilitating real-time monitoring and response to incidents. The review also addresses the critical need for collaboration between maritime and technology industries, emphasizing how partnerships can foster innovation and provide tailored solutions to the sector’s specific needs. Furthermore, the paper examines current implementations and case studies that illustrate successful applications of AI and automation in adverse maritime conditions. While recognizing the potential benefits, the review highlights the technical and operational challenges inherent in these implementations, including data integration, regulatory compliance, and cultural differences between sectors. Ultimately, this paper aims to provide a comprehensive overview of the state of maritime cybersecurity and the pivotal role of AI and automation in shaping a resilient, secure maritime future. The findings underscore the importance of ongoing research and development, collaborative efforts, and the necessity of adaptable strategies to safeguard maritime operations against the evolving landscape of cyber threats.

Transnational Cyber Governance for Risk Management in the Gas Sector: Exploring the Potential of G7 Cooperation

At the Group of Seven (G7) summit held on 13–15 June in 2024, the Group’s leaders committed to establishing a collective cyber security framework and reinforcing the work of the cyber security working group to manage the risks targeting energy systems. Likewise, oil and electricity, and natural gas rely on complex and interdependent technologies and communication networks from production to consumption. The preparedness to handle cyber security threats in the energy infrastructures among decision makers, planners, and the industry in a concerted manner signifies that cyber security is becoming more appreciated. Therefore, considering the ambition and achievement of the G7 countries towards energy and cyber sovereignty, this paper’s focus and research question aims to explore the potential existence of the cyber governance alliance in the gas subsector within the G7. The objective of this paper is twofold. First, it explores the potential of the G7, the world’s seven largest advanced economies, to lead on a nascent cyber governance for risk management in the gas sector. The qualitative analysis conducted through the institutional analysis and design method examines up-to-date data involving mainly state actors. Second, by drawing on LNG, one of the world’s fastest growing energy types in the coming decades, the paper points out the need for further research on the transnational governance operating through public–private engagement to address the cyber risks to gas systems. While the paper makes an empirical contribution to the field of security governance and a practical contribution to security consulting, its limitations rely on the necessity to also conduct a quantitative enquiry, which would necessitate, among others, a review of the literature in the G7 countries, and a group of researchers from academia and practitioners to obtain a sense of the cyberspace in the energy reality.

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.