A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discrete Cosine Transform(DCT),and Fast Fourier Transform(FFT)are considered and compared for the image communication task.The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels,while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels.

Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain management.Blockchain interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application exchanges.However,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain nodes.There is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous networks.Data security and privacy have also become the main concerns across the heterogeneous network to build smart IoT ecosystems.To address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with blockchain.The key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous network.This paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity systems.In addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among organizations.Overall,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it presents a potential cybersecurity design with key requirements of blockchain across a heterogeneous network.

ML-SPAs:Fortifying Healthcare Cybersecurity Leveraging Varied Machine Learning Approaches against Spear Phishing Attacks

Spear Phishing Attacks(SPAs)pose a significant threat to the healthcare sector,resulting in data breaches,financial losses,and compromised patient confidentiality.Traditional defenses,such as firewalls and antivirus software,often fail to counter these sophisticated attacks,which target human vulnerabilities.To strengthen defenses,healthcare organizations are increasingly adopting Machine Learning(ML)techniques.ML-based SPA defenses use advanced algorithms to analyze various features,including email content,sender behavior,and attachments,to detect potential threats.This capability enables proactive security measures that address risks in real-time.The interpretability of ML models fosters trust and allows security teams to continuously refine these algorithms as new attack methods emerge.Implementing ML techniques requires integrating diverse data sources,such as electronic health records,email logs,and incident reports,which enhance the algorithms’learning environment.Feedback from end-users further improves model performance.Among tested models,the hierarchical models,Convolutional Neural Network(CNN)achieved the highest accuracy at 99.99%,followed closely by the sequential Bidirectional Long Short-Term Memory(BiLSTM)model at 99.94%.In contrast,the traditional Multi-Layer Perceptron(MLP)model showed an accuracy of 98.46%.This difference underscores the superior performance of advanced sequential and hierarchical models in detecting SPAs compared to traditional approaches.

Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.

Enable Excel-Based Basic Cybersecurity Features for End Users by Using Python-Excel Integration

In the digital age, the global character of the Internet has significantly improved our daily lives by providing access to large amounts of knowledge and allowing for seamless connections. However, this enormously interconnected world is not without its risks. Malicious URLs are a powerful menace, masquerading as legitimate links while holding the intent to hack computer systems or steal sensitive personal information. As the sophistication and frequency of cyberattacks increase, identifying bad URLs has emerged as a critical aspect of cybersecurity. This study presents a new approach that enables the average end-user to check URL safety using Microsoft Excel. Using the powerful VirusTotal API for URL inspections, this study creates an Excel add-in that integrates Python and Excel to deliver a seamless, user-friendly interface. Furthermore, the study improves Excel’s capabilities by allowing users to encrypt and decrypt text communications directly in the spreadsheet. Users may easily encrypt their conversations by simply typing a key and the required text into predefined cells, enhancing their personal cybersecurity with a layer of cryptographic secrecy. This strategy democratizes access to advanced cybersecurity solutions, making attentive digital integrity a feature rather than a daunting burden.

Enhancing Cybersecurity through Cloud Computing Solutions in the United States

This study investigates how cybersecurity can be enhanced through cloud computing solutions in the United States. The motive for this study is due to the rampant loss of data, breaches, and unauthorized access of internet criminals in the United States. The study adopted a survey research design, collecting data from 890 cloud professionals with relevant knowledge of cybersecurity and cloud computing. A machine learning approach was adopted, specifically a random forest classifier, an ensemble, and a decision tree model. Out of the features in the data, ten important features were selected using random forest feature importance, which helps to achieve the objective of the study. The study’s purpose is to enable organizations to develop suitable techniques to prevent cybercrime using random forest predictions as they relate to cloud services in the United States. The effectiveness of the models used is evaluated by utilizing validation matrices that include recall values, accuracy, and precision, in addition to F1 scores and confusion matrices. Based on evaluation scores (accuracy, precision, recall, and F1 scores) of 81.9%, 82.6%, and 82.1%, the results demonstrated the effectiveness of the random forest model. It showed the importance of machine learning algorithms in preventing cybercrime and boosting security in the cloud environment. It recommends that other machine learning models be adopted to see how to improve cybersecurity through cloud computing.

Closing the Gap: Boosting Women’s Representation in Cybersecurity Leadership

The research consistently highlights the gender disparity in cybersecurity leadership roles, necessitating targeted interventions. Biased recruitment practices, limited STEM education opportunities for girls, and workplace culture contribute to this gap. Proposed solutions include addressing biased recruitment through gender-neutral language and blind processes, promoting STEM education for girls to increase qualified female candidates, and fostering inclusive workplace cultures with mentorship and sponsorship programs. Gender parity is crucial for the industry’s success, as embracing diversity enables the cybersecurity sector to leverage various perspectives, drive innovation, and effectively combat cyber threats. Achieving this balance is not just about fairness but also a strategic imperative. By embracing concerted efforts towards gender parity, we can create a more resilient and impactful cybersecurity landscape, benefiting industry and society.

Scale, Complexity, and Cybersecurity Risk Management

Elementary information theory is used to model cybersecurity complexity, where the model assumes that security risk management is a binomial stochastic process. Complexity is shown to increase exponentially with the number of vulnerabilities in combination with security risk management entropy. However, vulnerabilities can be either local or non-local, where the former is confined to networked elements and the latter results from interactions between elements. Furthermore, interactions involve multiple methods of communication, where each method can contain vulnerabilities specific to that method. Importantly, the number of possible interactions scales quadratically with the number of elements in standard network topologies. Minimizing these interactions can significantly reduce the number of vulnerabilities and the accompanying complexity. Two network configurations that yield sub-quadratic and linear scaling relations are presented.

AssessITS: Integrating Procedural Guidelines and Practical Evaluation Metrics for Organizational IT and Cybersecurity Risk Assessment

In today’s digitally driven landscape, robust Information Technology (IT) risk assessment practices are essential for safeguarding systems, digital communication, and data. This paper introduces “AssessITS,” an actionable method designed to provide organizations with comprehensive guidelines for conducting IT and cybersecurity risk assessments. Drawing extensively from NIST 800-30 Rev 1, COBIT 5, and ISO 31000, “AssessITS” bridges the gap between high-level theoretical standards and practical implementation challenges. The paper outlines a step-by-step methodology that organizations can simply adopt to systematically identify, analyze, and mitigate IT risks. By simplifying complex principles into actionable procedures, this framework equips practitioners with the tools needed to perform risk assessments independently, without too much reliance on external vendors. The guidelines are developed to be straightforward, integrating practical evaluation metrics that allow for the precise quantification of asset values, threat levels, vulnerabilities, and impacts on confidentiality, integrity, and availability. This approach ensures that the risk assessment process is not only comprehensive but also accessible, enabling decision-makers to implement effective risk mitigation strategies customized to their unique operational contexts. “AssessITS” aims to enable organizations to enhance their IT security strength through practical, actionable guidance based on internationally recognized standards.

A Review of Cybersecurity Challenges in Small Business: The Imperative for a Future Governance Framework

Technological shifts—coupled with infrastructure, techniques, and applications for big data—have created many new opportunities, business models, and industry expansion that benefit entrepreneurs. At the same time, however, entrepreneurs are often unprepared for cybersecurity needs—and the policymakers, industry, and nonprofit groups that support them also face technological and knowledge constraints in keeping up with their needs. To improve the ability of entrepreneurship research to understand, identify, and ultimately help address cybersecurity challenges, we conduct a literature review on the state of cybersecurity. The research highlights the necessity for additional investigation to aid small businesses in securing their confidential data and client information from cyber threats, thereby preventing the potential shutdown of the business.

Enhancing Cybersecurity through AI and ML: Strategies, Challenges, and Future Directions

The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed. The paper emphasizes a balanced approach that leverages technological innovation alongside rigorous ethical standards and robust cybersecurity practices. This approach facilitates collaboration among various stakeholders to develop guidelines that ensure responsible and effective use of AI in cybersecurity, aiming to enhance system integrity and privacy without compromising security.

Artificial Intelligence Adoption for Cybersecurity in Africa

Legacy-based threat detection systems have not been able to keep up with the exponential growth in scope, frequency, and effect of cybersecurity threats. Artificial intelligence is being used as a result to help with the issue. This paper’s primary goal is to examine how African nations are utilizing artificial intelligence to defend their infrastructure against cyberattacks. Artificial intelligence (AI) systems will make decisions that impact Africa’s future. The lack of technical expertise, the labor pool, financial resources, data limitations, uncertainty, lack of structured data, absence of government policies, ethics, user attitudes, insufficient investment in research and development, and the requirement for more adaptable and dynamic regulatory systems all pose obstacles to the adoption of AI technologies in Africa. The paper discusses how African countries are adopting artificial intelligence solutions for cybersecurity. And it shows the impact of AI to identify shadow data, monitor for abnormalities in data access and alert cyber security professionals about potential threats by anyone accessing the data or sensitive information saving valuable time in detecting and remediating issues in real-time. The study finds that 69.16% of African companies are implementing information security strategies and of these, 45% said they use technologies based on AI algorithms. This study finds that a large number of African businesses use tools that can track and analyze user behaviour in designated areas and spot anomalies, such as new users, strange IP addresses and login activity, changes to permissions on files, folders, and other resources, and the copying or erasure of massive amounts of data. Thus, we discover that just 18.18% of the target has no national cybersecurity strategy or policy. The study proposes using big data security analytics to integrate AI. Adopting it would be beneficial for all African nations, as it provides a range of cyberattack defense techniques.

Navigating AI Cybersecurity: Evolving Landscape and Challenges

The rapid integration of artificial intelligence (AI) into critical sectors has revealed a complex landscape of cybersecurity challenges that are unique to these advanced technologies. AI systems, with their extensive data dependencies and algorithmic complexities, are susceptible to a broad spectrum of cyber threats that can undermine their functionality and compromise their integrity. This paper provides a detailed analysis of these threats, which include data poisoning, adversarial attacks, and systemic vulnerabilities that arise from the AI’s operational and infrastructural frameworks. This paper critically examines the effectiveness of existing defensive mechanisms, such as adversarial training and threat modeling, that aim to fortify AI systems against such vulnerabilities. In response to the limitations of current approaches, this paper explores a comprehensive framework for the design and implementation of robust AI systems. This framework emphasizes the development of dynamic, adaptive security measures that can evolve in response to new and emerging cyber threats, thereby enhancing the resilience of AI systems. Furthermore, the paper addresses the ethical dimensions of AI cybersecurity, highlighting the need for strategies that not only protect systems but also preserve user privacy and ensure fairness across all operations. In addition to current strategies and ethical concerns, this paper explores future directions in AI cybersecurity.

Cybersecurity Challenge in Nigeria Deposit Money Banks

The study investigates cybersecurity challenges in Nigerian deposit money banks (DMBs) with a focus on proactive measures taken by banks and customers to overcome these challenges. The research design employs a descriptive approach and census sampling, with data collected from staff of selected DMBs using questionnaires. Data analysis was conducted using SPSS, and findings indicate that the major challenges confronting cybersecurity in banks were pharming, identity theft, SIM Swap fraud, Skimming/Website cloning and Smishing/Vishing. The major factors responsible were found to include loopholes in the banks’ internal control system, insider abuse by bank staff, ignorance and lack of security consciousness among the banking customers etc. it was found that banks implement measures such as encryption, password changes, and blocking unsolicited messages to mitigate cybersecurity risks. The study concludes with recommendations for continuous security updates, internal control reviews, and customer education campaigns. While the study addresses an important topic, there are areas where clarity, depth, and methodological rigor could be strengthened for a more robust contribution to the field.

Closing the Cybersecurity Skill Gap in Kenya: Curriculum Interventions in Higher Education

In the wake of increased cybercrime against insufficient cybersecurity professionals, there is an urgent need to bridge the skill-gap. The demand for skilled and experienced (approximately 40,000 to 50,000) cybersecurity professionals in Kenya is soaring all-time high. This demand is against the available 1700 certified professionals. Therefore, this paper seeks to bring to fore interventions put in place to address the skill gap through curriculum interventions. In order to get a clear understanding, the paper sought to determine the status of cybersecurity skill gap in Kenya and what universities are doing to address the gap. The paper also sought to propose the way forward to close the skill gap. This is a seminal review paper in the field of cybersecurity in Kenya focusing on institutions of higher learning and the interventions to address the cybersecurity skill gap. This research is significant to the general institutions of higher learning in both private and public universities. Results show that the cybersecurity skill gap is very high in Kenya. Interventions being offered by universities include partnerships with private cybersecurity organizations, offering cybersecurity certification training hackathons, and degree programs. However, it was established that only 13.2% of registered universities that offer cybersecurity degree programs in Kenya. The paper therefore strongly recommends launch of cybersecurity programs at the levels of undergraduate and graduate in many universities. This can therefore be augmented with other interventions such as certifications, hackathons and partnerships. Further research can be conducted to establish factors affecting the launch of cybersecurity programs in institutions of higher learning in Kenya. A further research can also be conducted to determine the effect of supplementary cybersecurity trainings such as hackathons and certifications.

Using Event-Based Method to Estimate Cybersecurity Equilibrium

Estimating the global state of a networked system is an important problem in many application domains.The classical approach to tackling this problem is the periodic(observation)method,which is inefficient because it often observes states at a very high frequency.This inefficiency has motivated the idea of event-based method,which leverages the evolution dynamics in question and makes observations only when some rules are triggered(i.e.,only when certain conditions hold).This paper initiates the investigation of using the event-based method to estimate the equilibrium in the new application domain of cybersecurity,where equilibrium is an important metric that has no closed-form solutions.More specifically,the paper presents an event-based method for estimating cybersecurity equilibrium in the preventive and reactive cyber defense dynamics,which has been proven globally convergent.The presented study proves that the estimated equilibrium from our trigger rule i)indeed converges to the equilibrium of the dynamics and ii)is Zeno-free,which assures the usefulness of the event-based method.Numerical examples show that the event-based method can reduce 98%of the observation cost incurred by the periodic method.In order to use the event-based method in practice,this paper investigates how to bridge the gap between i)the continuous state in the dynamics model,which is dubbed probability-state because it measures the probability that a node is in the secure or compromised state,and ii)the discrete state that is often encountered in practice,dubbed sample-state because it is sampled from some nodes.This bridge may be of independent value because probability-state models have been widely used to approximate exponentially-many discrete state systems.

Impact of Human Vulnerabilities on Cybersecurity

Today,security is a major challenge linked with computer network companies that cannot defend against cyber-attacks.Numerous vulnerable factors increase security risks and cyber-attacks,including viruses,the internet,communications,and hackers.Internets of Things(IoT)devices are more effective,and the number of devices connected to the internet is constantly increasing,and governments and businesses are also using these technologies to perform business activities effectively.However,the increasing uses of technologies also increase risks,such as password attacks,social engineering,and phishing attacks.Humans play a major role in the field of cybersecurity.It is observed that more than 39%of security risks are related to the human factor,and 95%of successful cyber-attacks are caused by human error,with most of them being insider threats.The major human factor issue in cybersecurity is a lack of user awareness of cyber threats.This study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different situations.This study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal information.Moreover,the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity,such as phishing attacks,passwords,attacks,and social engineering,are major problems that need to be addressed and reduced through proper awareness and training.

Development of an Analytical Model of the Process of Cybersecurity Protection of Distributed Information Systems of Critical Infrastructure

This article is dedicated to the creation of the analytical model of quantitative estimation of cybersecurity of Information Systems of Critical Infrastructure (ISCI). The model takes into consideration the existence, in the discussed ISCI, of both the intelligent tools of detection, analysis and identification of threats and vulnerabilities and means for restauration and elimination of their consequences. The development of the model also takes into consideration probabilistic nature of flow of events happening in ISCI and transferring the system between different states of cybersecurity. Among such probabilistic events we mean any operational perturbations (that can cause extreme situations) happening in ISCI under the influence of cyber-threats, as well as events concerning restoration and elimination of consequences of such cyber-threats. In this work, as methods of modelling, there have been used methods of system-oriented analysis based on theory of probability, theory of reliability and theory of queues. These methods enabled to describe analytically dependence of effectiveness indices of ISCI operation on abovementioned probabilistic processes.

Investigating How Parental Perceptions of Cybersecurity Influence Children’s Safety in the Cyber World: A Case Study of Saudi Arabia

This paper explores the convergence of Saudi Arabia’s Vision 2030 with the increasing dependence on the Internet for educational purposes. It sheds light on the potential cybersecurity risks and how parental perception impacts children’s willingness to adapt cybersecurity features. By instilling the significance of cybersecurity awareness in early stages, society can provide children with the necessary skills to navigate the digital realm responsibly. As we progress, ongoing research and collaborative endeavors will be pivotal in formulating effective strategies to shield the digital generation from the potential pitfalls of the virtual realm. Regular Internet usage is essential for various purposes such as communication, education, and leisure. The cohorts of Generation Z and Alpha were born during a period of exponential Internet growth, leading them to heavily engage with the Internet. Consequently, they are equally vulnerable to cybersecurity threats just like adults. Addressing potential security risks for today’s youth becomes the responsibility of parents as the primary line of defense. This research focuses on raising awareness about the imperative of ensuring children’s safety in the online sphere, particularly by their parents. The study is conducted within the specific context of Saudi Arabia, aiming to examine how Saudi parents’ perception of cybersecurity influences their children’s cyber safety. The study identifies critical factors, including attitudes towards cybersecurity, awareness of cybersecurity, and prevailing social norms regarding cybersecurity. These factors contribute to the development of parents’ intention to prioritize cybersecurity, which consequently affects their children’s behaviors in the digital realm. Utilizing a quantitative approach based on a questionnaire, the study employs a Structural Equation Modeling (SEM) framework to analyze the collected data. The study’s findings underscore that parents’ intent towards cybersecurity plays a significant role in shaping their children’s behavior concerning cyber safety.

The Role of Social Engineering in Cybersecurity and Its Impact

An attacker has several options for breaking through an organization’s information security protections. Human factors are determined to be the source of some of the worst cyber-attacks every day in every business. The human method, often known as “social engineering”, is the hardest to cope with. This paper examines many types of social engineering. The aim of this study was to ascertain the level of awareness of social engineering, provide appropriate solutions to problems to reduce those engineering risks, and avoid obstacles that could prevent increasing awareness of the dangers of social engineering—Shaqra University (Kingdom of Saudi Arabia). A questionnaire was developed and surveyed 508 employees working at different organizations. The overall Cronbach’s alpha was 0.756, which very good value, the correlation coefficient between each of the items is statistically significant at 0.01 level. The study showed that 63.4% of the surveyed sample had no idea about social engineering. 67.3% of the total samples had no idea about social engineering threats. 42.1% have a weak knowledge of social engineering and only 7.5% of the sample had a good knowledge of social engineering. 64.7% of the male did not know what social engineering is. 68.0% of the administrators did not know what social engineering is. Employees who did not take courses showed statistically significant differences.