Cybersecurity Perspective Research Articles

Assessing student readiness for mobile learning from a cybersecurity perspective

This research aims to measure student readiness in implementing mobile learning from a cybersecurity perspective. Using a quantitative method with a survey approach, the participants in this research were 150 students of the electrical engineering study program at Padang State University in Indonesia, who were randomly stratified to ensure a balanced representation of the academic year. The research is an online objective test related to cybersecurity topics. The data analysis technique used is quantitative descriptive. The analysis results show that system updates are the only indicator with a “good” awareness level, while other indicators are at the “sufficient” and “poor” levels, indicating the need for further improvement. These findings underscore the importance of integrating cyber security education in mobile learning curricula to increase student readiness to face evolving cyber threats. Thus, this research recommends developing specific training programs and adding comprehensive cybersecurity material to the curriculum to equip students with the skills necessary to maintain cybersecurity effectively.

Effectiveness of social engineering awareness training in mitigating spear phishing risks in financial institutions from a cybersecurity perspective

Spear phishing remains a critical cybersecurity threat to financial institutions, where attackers exploit human vulnerabilities to breach sensitive systems. This review paper explores the effectiveness of social engineering awareness training programs in reducing spear phishing risks within the financial sector from a cybersecurity perspective. By analyzing existing research, the paper assesses various training approaches, focusing on elements such as content relevance, delivery methods, and employee engagement. The review highlights how targeted awareness programs can enhance employees' ability to recognize and respond to phishing attempts, thereby strengthening overall cybersecurity defenses. The findings emphasize the importance of continuous, specialized training in fostering a proactive security culture and offer recommendations for optimizing awareness strategies to bolster cybersecurity resilience in financial institutions.

The Role of Internet of Battlefield Things in Modern Warfare: A Cybersecurity Perspective

The Internet of Battlefield Things (IoBT) is revolutionizing military operations by integrating interconnected devices and autonomous systems into combat environments, significantly enhancing decision-making, communication, and battlefield efficiency. However, this reliance on IoBT introduces substantial cybersecurity risks, exposing military networks to sophisticated cyber threats such as hacking, malware, and Distributed Denial of Service (DDoS) attacks. This paper explores the interplay between IoBT and the cyber domain, identifying the key challenges, vulnerabilities, and solutions necessary to secure these interconnected systems in the context of modern warfare. The study reviews the architectural components of IoBT, including sensors, drones, and communication networks, and examines their potential weaknesses in the face of cyberattacks. Additionally, it discusses common attack vectors such as Man-in-the-Middle (MitM) and zero-day exploits, which pose significant risks to military systems. To counter these threats, the paper evaluates various cybersecurity strategies, including the application of artificial intelligence (AI), machine learning (ML), blockchain technology, and robust encryption protocols designed to enhance the resilience and security of IoBT networks. Case studies of real-world IoBT deployments and cyber breaches further illustrate the practical implications of these challenges and defense mechanisms.

SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems

Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Systematic application of traffic‐signal‐control system architecture design and selection using model‐based systems engineering and Pareto frontier analysis

AbstractThe global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development.

Black-box adversarial transferability: An empirical study in cybersecurity perspective

The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial attacks is one of the major issues. In adversarial machine learning, malicious users try to fool the deep learning model by inserting adversarial perturbation inputs into the model during its training or testing phase. Subsequently, it reduces the model confidence score and results in incorrect classifications. The novel key contribution of the research is to empirically test the black-box adversarial transferability phenomena in cyber attack detection systems. It indicates that the adversarial perturbation input generated through the surrogate model has a similar impact on the target model in producing the incorrect classification. To empirically validate this phenomenon, surrogate and target models are used. The adversarial perturbation inputs are generated based on the surrogate-model for which the hacker has complete information. Based on these adversarial perturbation inputs, both surrogate and target models are evaluated during the inference phase. We have done extensive experimentation over the CICDDoS-2019 dataset, and the results are classified in terms of various performance metrics like accuracy, precision, recall and f1-score. The findings indicate that any deep learning model is highly susceptible to adversarial attacks, even if the attacker does not have access to the internal details of the target model. The results also indicate that white-box adversarial attacks have a severe impact compared to black-box adversarial attacks. There is a need to investigate and explore adversarial defence techniques to increase the robustness of the deep learning models against adversarial attacks.

Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective

Why disinformation is a cyber threat.

Effect of AI

Abstract: Artificial intelligence (AI) is considered a vital factor that will fundamentally alter the cybersecurity environment. AI technology is progressing much faster than expected, and AI-based security services are being introduced into the global security market on a daily basis. However, how AI can contribute to the cybersecurity field and what changes it will bring remain unknown. Nonetheless, cybersecurity is not merely a technical issue but also a process for dealing with regulations, policies, and security risks; therefore, the introduction of AI technology introduction can make a fundamental difference in cybersecurity policy as a whole. This study primarily aims to better understand the concept and characteristics of AI from the cybersecurity perspective and identify its future implications on cybersecurity environment at the national policy level. This study predicts what modifications will be made to national cybersecurity strategies (NCSS) when machine learning (ML) is introduced and implemented. It also provides a basic policy recommendation that offers potential responses to these changes. The study first describes the emergence of AI in the cybersecurity field and explains AI-ML technical services and AI security policy elements. Second, through NCSS material analysis, this study categorizes NCSS into 11 categories and selects the critical functions of each dimension. Finally, it predicts the changes that will occur when AI is introduced within the selected NCSS category. It also introduces the priorities and considerations required for these changes.

Blockchain for Artificial Intelligence (AI): enhancing compliance with the EU AI Act through distributed ledger technology. A cybersecurity perspective

The article aims to investigate the potential of blockchain technology in mitigating certain cybersecurity risks associated with artificial intelligence (AI) systems. Aligned with ongoing regulatory deliberations within the European Union (EU) and the escalating demand for more resilient cybersecurity measures within the realm of AI, our analysis focuses on specific requirements outlined in the proposed AI Act. We argue that by leveraging blockchain technology, AI systems can align with some of the requirements in the AI Act, specifically relating to data governance, record-keeping, transparency and access control. The study shows how blockchain can successfully address certain attack vectors related to AI systems, such as data poisoning in trained AI models and data sets. Likewise, the article explores how specific parameters can be incorporated to restrict access to critical AI systems, with private keys enforcing these conditions through tamper-proof infrastructure. Additionally, the article analyses how blockchain can facilitate independent audits and verification of AI system behaviour. Overall, this article sheds light on the potential of blockchain technology in fortifying high-risk AI systems against cyber risks, contributing to the advancement of secure and trustworthy AI deployments. By providing an interdisciplinary perspective of cybersecurity in the AI domain, we aim to bridge the gap that exists between legal and technical research, supporting policy makers in their regulatory decisions concerning AI cyber risk management.

Risk sensitivity analysis of AIS cyber security through maritime cyber regulatory frameworks

Given the increasing frequency and sophistication of methods and strategies employed in cyberattacks, cyber resilience has become a basic notion of cyber risk management. To be cyber-resilient against cyber risks, shipping companies must be proactive in establishing and implementing actions, constructing effective strategies, and adopting mitigation methods to strengthen their assets. However, shipping companies have only lately tended to fully recognize the necessity for a cybersecurity perspective to enable effective cyber risk management and mitigation of increasing cyberattacks. Aside from deficiencies in system design, integration, or maintenance, human factors are the prime weakness that potentially jeopardizes the ship's cybersecurity by simply making intentional or unintentional errors, revealing critical information, or generating entry points for attackers. Therefore, the current study aims to conduct a quantitative human risk assessment based on the SOHRA method, which is integrated with the NIST cybersecurity framework, to provide ships with the ability to be cyber resilient, and respond to and recover from cyber-attacks. The AIS has been considered for the research application not only because it is one of the most vulnerable systems on board a ship, but also because modifying and breaching the AIS data might have disastrous outcomes. The study results clearly indicate that the most likely error related to AIS cybersecurity risk occurs in the tasks defined under the "protect", "respond", "detect", "identify", and "recover" functions. Accordingly, suitable control and preventative measures have been developed to guarantee high-level cyber security for AIS and to provide cyber resilience and the structure for constructive decision-making by integrating various international standards, which include system security requirements and security levels for industrial communication networks, specifically with the IACS and NIST framework for the AIS cyber security.

Chance-constrained vulnerability assessment of smart water distribution systems against stealthy false data injection attacks

Smart water distribution systems (SWDSs) have not only enhanced the monitoring and control of the entire water infrastructure but have also jeopardized its security and resilience. The state-of-the-art examined the uncertainties in the water supply network to obtain optimal design parameters to ensure hydraulic integrity. However, the investigations of uncertainties in SWDSs from the cybersecurity perspective remain unexplored. To address this limitation, this paper utilizes a robust chance-constrained optimization strategy to scrutinize the vulnerable location of SWDSs against False Data Injection (FDI) attacks. This is achieved by factoring in the probabilistic behavior of water demand at junctions that can potentially facilitate stealthy FDI attacks on the hourly measurements of the pump’s flow rate. The proposed nonlinear model is validated with several case studies reflecting the real-world cyberattack consequences including cutoff water supply of the network, enhanced power consumption of the pump, and the undesirable pressure surge in the system at strategic locations of SWDSs. As a result of the studied cases, the cyberattack consequences on the entire water supply network are obtained while highlighting the threat-prone regions of the network. This paper contributes to embedding additional security layers to the existing water infrastructure at the vulnerable links and junctions.

A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

DAEMON: A Domain-Based Monitoring Ontology for IoT Systems

Internet of Things (IoT) is an emerging technology used in several contexts and domains. The work aims to define a technological reference solution specifically conceived for monitoring and assessing the behavior of IoT systems from the cybersecurity perspective when a new device or component joins the system. We leverage semantic web technologies, such as ontologies, for defining DAEMON, a domain-based ontology that formally models monitoring, IoT, and System of Systems (SoS) domains’ knowledge. We also propose a supporting architecture and describe the proof-of-concept implementing different components. We have validated and showcased our proposal by instantiating DAEMON into a multi-robot autonomous navigation scenario applied to the intralogistics domain.

Achieving Sustainable Development Goals from a Cybersecurity Perspective

& Department of Computer Science, University of Abuja, Nigeria Global Digital Innovation (GDI-GITTP), Korea Advance Institute of Science and Technology (KAIST), South Korea E-mails: olayemijohn@yahoo.com; boyorules@gmail.com ABSTRACT Rapid digitalisation and society's interconnection have provided both opportunities and problems for sustainable development, as such, this paper aims to understand how cybersecurity measures might help achieve Sustainable Development Goals (SDGs). As more critical infrastructure, key services, and personal data are kept and sent via digital networks, the significance of cybersecurity in accomplishing the SDGs becomes clear. Through a comprehensive literature review, the paper analyses the role of cybersecurity in enhancing economic growth, promoting social inclusivity, and safeguarding environmental sustainability. The paper thoroughly analyses the seventeen (17) SDGs listed by the United Nations (UN) and their underlying goals. The paper looks at how cybersecurity relates to the SDGs, emphasising potential synergies and interdependencies. Finally, this paper emphasises the critical importance of recognising the interdependence between cybersecurity and sustainable development. By mapping cybersecurity with the SDGs, societies can harness the transformative power of digital technologies to build a secure, inclusive, and sustainable future for all. Keywords: SDGs, Cybersecurity, Personal Data, Critical Infrastructure, United Nations, Cyber Threats Proceedings Citation Format Odumesi, J.O. & Sanusi, B.S. (2023): Achieving Sustainable Development Goals from a Cybersecurity Perspective. Proceedings of the Cyber Secure Nigeria Conference. Nigerian Army Resource Centre (NARC) Abuja, Nigeria. 11-12th July, 2023. Pp 1-10 https://www.csean.org.ng/. dx.doi.org/10.22624/AIMS/CSEAN-SMART2023P3

Strenghtening Resilience in Digital Climate Diplomacy: A Cybersecurity Perspective

Strenghtening Resilience in Digital Climate Diplomacy: A Cybersecurity Perspective

A Cyber Counterintelligence Competence Framework: Developing the Job Roles

In recent years, there have been intensifying cyber risks and volumes of cyber incidents prompting a significant shift in the cyber threat landscape. Both nation-state and non-state actors are increasingly resolute and innovative in their techniques and operations globally. These intensifying cyber risks and incidents suggest that cyber capability is inversely proportional to cyber risks, threats and attacks. Therefore, this confirms an emergent and critical need to adopt and invest in intelligence strategies, predominantly cyber counterintelligence (CCI), which is a multi-disciplinary and proactive measure to mitigate risks and counter cyber threats and cyber-attacks. Concurrent with the adoption of CCI is an appreciation that requisite job roles must be defined and developed. Notwithstanding the traction that CCI is gaining, we found no work on a clear categorisation for the CCI job roles in the academic or industry literature surveyed. Furthermore, from a cybersecurity perspective, it is unclear which job roles constitute the CCI field.
 This paper stems from and expands on the authors’ prior research on developing a CCI Competence Framework. The proposed CCI Competence Framework consists of four critical elements deemed essential for CCI workforce development. In order of progression, the Framework’s elements are: CCI Dimensions (passive-defensive, active-defensive, passive-offensive, active-offensive), CCI Functional Areas (detection, deterrence, deception, neutralisation), CCI Job Roles (associated with each respective Functional Area), and Tasks and Competences (allocated to each job role). Pivoting on prior research on CCI Dimensions and CCI Functional Areas, this paper advances a proposition on associated Job Roles in a manner that is both intelligible and categorised.
 To this end, the paper advances a five-step process that evaluates and examines Counterintelligence and Cybersecurity Job Roles and functions to derive a combination of new or existing Job Roles required for the CCI workforce/professionals. Although there are several cybersecurity frameworks for workforce development, establishing the CCI Job Roles is specifically based on the expression of the Job Roles defined in the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework.

Analysing the Connection Between AI and Industry 4.0 from a Cybersecurity Perspective: Defending the Smart Revolution

In recent years, the significance and efficiency of business performance have become dependent heavily on digitization, as jobs in companies are seeking to be transformed into digital jobs based on smart systems and applications of the fourth industrial revolution. Cybersecurity systems must interact and continuously cooperate with authorized users through the Internet of Things and benefit from corporate services that allow users to interact in a secure environment free from electronic attacks. Artificial intelligence methods contribute to the design of the Fourth Industrial Revolution principles, including interoperability, information transparency, technical assistance, and decentralized decisions. Through this design, security gaps may be generated that attackers can exploit in order to be able to enter systems, control them, or manipulate them. In this paper, the role of automated systems for digital operations in the fourth industrial revolution era will be examined from the perspective of artificial intelligence and cybersecurity, as well as the most significant practices of artificial intelligence methods. This paper concluded that artificial intelligence methods play a significant role in defending and protecting cybersecurity and the Internet of Things, preventing electronic attacks, and protecting users' privacy.

An Exploratory Study of Mobile App Use by Financial Institutions: Cybersecurity Perspective

An Exploratory Study of Mobile App Use by Financial Institutions: Cybersecurity Perspective