Cybersecurity Infrastructure Research Articles

Transforming Cybersecurity Infrastructure : A Multi-dimensional Approach to Risk, Culture, and Technological Integration

The rapid acceleration of digital transformation has exposed critical limitations in traditional cybersecurity approaches, particularly in their reactive nature and disconnection from broader organizational strategies. This article critically examines current cybersecurity practices in IT infrastructure management, identifying significant gaps in the integration of security measures with business objectives, organizational culture, and emerging technologies. Through a mixed-method analysis of industry practices and empirical data from multiple case studies, The article proposes a comprehensive framework that transcends conventional security paradigms. The article introduces a proactive, risk-based approach that integrates cultural transformation, emerging technologies, and resilience building while fostering strategic partnerships across stakeholder groups. Initial implementation across various organizational contexts demonstrates significant improvements in security posture, incident response times, and overall business alignment. The findings contribute to both theoretical understanding and practical application of integrated cybersecurity management, offering valuable insights for practitioners and researchers in the field of IT security and organizational resilience. This article addresses a critical gap in current literature by providing a holistic approach that aligns cybersecurity initiatives with organizational transformation while considering human factors and technological evolution.

To Study on Cyber Crime Burgeoning in India and Crucial Actions Need for Every Cybercrime Victim

India is facing a great deal of challenges due to the rapid digitalization and broad adoption of technology in many different areas. One such concern is cybercrime. In this paper explores the nature, scope, and impact of cybercrime in the Indian context. The rising ubiquity of internet usage has rendered people, companies, and governmental organizations more susceptible to a range of cyber threats, such as identity theft, financial fraud, data breaches, and cyber harassment. Despite developments in cyber security protection, the lack of awareness and poor regulatory frameworks has aggravated the situation. To address these issues, the Indian government has put in place a number of efforts, including the National Cyber Security Policy and the Information Technology Act, 2000. But enforcement is still patchy, and a lot of crimes go unreported or ignored. The problem has been made worse by insufficient regulatory frameworks and a lack of knowledge, even in spite of advances in cyber security measures. But enforcement is still patchy, and a lot of crimes go unreported or ignored. This research paper emphasizes India's cybercrime victims have several legal options, the need for a multimodal strategy that includes improved cyber security infrastructure, better legislative frameworks and greater public awareness in order to tackle cybercrime. In light of the transnational character of cyber threats, it also highlights the significance of international cooperation. In the end, a thorough plan is necessary to protect India's digital environment and guarantee the security and safety of its people in a world that is becoming more interconnected by the day

Navigating Change: A Comprehensive Analysis of Current Financial Trends in the United States

This research study offers a comprehensive overview of current advancements in financial practices in the United States. This research will examine recent shifts in American financial habits and offer stakeholders guidance on how to effectively manage the evolving financial landscape. A thorough assessment of prior literature reviews and empirical studies on digital finance in the US is part of the research methodology.The literature review focuses on how developments in financial technology (FinTech), regulatory changes, a growing emphasis on sustainability, and shifting consumer behavior have significantly altered the financial sector.. The influence of regulatory barriers, ESG integration, evolving consumer behavior, and the complex interactions affecting US financial practices are the main topics of discussion. The results underscore the significance of digital transformation, regulatory impediments and campaigns, consumer inclinations, the advantages and challenges of decentralized financing (DeFi), and cybersecurity and privacy issues. Recommendations are provided based on the results to enhance regulatory flexibility, raise financial literacy and awareness, fund cybersecurity infrastructure, encourage cooperation and information exchange, welcome responsible innovation, and track and react to market dynamics. By putting these recommendations into practice, stakeholders can better navigate the complexity of digital banking in the US and foster innovation, inclusion, and trust in the digital financial ecosystem while averting the dangers and difficulties that come with it.

Ejection of Real-Time Malicious Intrusion and Attacks in IoT Empowered Infrastructure

The project recognizes the pervasive threat of computer viruses, malware, and hostile attacks on computer networks, highlighting the critical role of intrusion detection as a proactive defense technology. The project introduces a novel approach based on deep learning to identify and mitigate cybersecurity vulnerabilities and breaches in IoT-driven cyber-physical systems, aiming for enhanced security measures. The project's objective is to elevate intrusion detection beyond the limitations of traditional systems by addressing issues like accuracy, detection effectiveness, and reducing false positives. This emphasizes the advancement and innovation in cybersecurity. To achieve the project's goals, the method employs a generative adversarial network, a cutting-edge deep learning technique. Additionally, it distinguishes itself by contrasting unsupervised and deep learning-based discriminative approaches, showcasing a comprehensive and effective approach to cybersecurity. In our project, we successfully implemented an ensemble method to boost predictive accuracy by integrating multiple individual models. Particularly noteworthy is the inclusion of a hybrid architecture, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), denoted as CNN+LSTM. This hybrid model achieved an impressive accuracy of 99% when applied to the KDD-Cup dataset, underscoring the efficacy of our ensemble technique for intrusion detection in IoT- based cybersecurity infrastructures.

Cybersecurity Tool Rationalization: A Strategic Approach to Optimizing Cybersecurity Infrastructure with Machine Learning Integration

Cybersecurity Tool Rationalization: A Strategic Approach to Optimizing Cybersecurity Infrastructure with Machine Learning Integration

New Heuristics Method for Malicious URLs Detection Using Machine Learning

Malicious URLs are a very prominent, dangerous form of cyber threats in view of the fact that they can enable many evils like phishing attacks, malware distribution, and several other kinds of cyber fraud. The techniques of detection conventionally applied are based on blacklisting and heuristic analyses, which are gradually becoming inefficient against sophisticated, rapidly evolving threats. In this paper, the authors present various machine learning techniques applied in malicious URL detection. In the present paper, we will look at three machine learning models: Logistic Regression, Random Forest, and Support Vector Machines. We used a methodology that involved collecting data and feature extraction, training a model, then evaluating its performance with different metrics such as accuracy, precision, recall, and F1-score. We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. Logistic Regression shows promising results to detect the malicious URLs, according to Vanitha and Vinodhini. Random Forest models are found to be very robust and accurate according to Cui et al. and Vanhoenshoven et al., SVM models are evidenced to have very high accuracy according to Manjeri et al., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. All the optimization processes, model performances, and integration into the real-time cybersecurity infrastructures, along with the strengths and limitations, are discussed in this paper. The paper will, therefore, discuss the benefits and challenges for each model in this aspect—emphasizing continuous updating of the models and integrating them into real-time cybersecurity infrastructures.

Cybersecurity leadership policy and compliance for institutions of higher education

Cybersecurity pairs with information asset storage and security for institutions of higher education. When institutions of higher education lack effective cybersecurity leadership, cyber incidents escalate. Worldwide, institutions of higher education are experiencing threats and vulnerabilities woven in their information systems and technology applications. This requires strategic resolutions. One strategy is cybersecurity and information security policy. Research collected worldwide proves usage of cybersecurity policy aligned with federal international law, establishes a cyber culture of compliance. Furthermore, personnel, internally and externally, are the bridge to managing access to data assets, and unless they are knowledgeable of the policy mandated by the institution and its cybersecurity infrastructure, the results of non-compliance can be catastrophic. Since the United States National Institute of Standards and Technology enacted cybersecurity policy for federal information systems. Institutions of higher education must adhere to these standards and guidelines to manage threats and vulnerabilities programmed in their hardware, software, and cloud. Therefore, why are so many institutions of higher education neglecting to align policy and enforcement of policy with these standards? What problems are creating non-compliance among institutions of higher education personnel and executive leadership? How can cybersecurity leadership improve policy and compliance to control risks factors programmed in information systems, technology, and cloud applications, institutions of higher education rely on to reduce human error? To assess and answer these questions, the researcher deployed qualitative grounded theory lite research to assess trends of cybersecurity leadership at institutions of higher education and to improve policy development and compliance.

TDBAMLA: Temporal and dynamic behavior analysis in Android malware using LSTM and attention mechanisms

The increasing ubiquity of Android devices has precipitated a concomitant surge in sophisticated malware attacks, posing critical challenges to cybersecurity infrastructures worldwide. Existing models have achieved significant strides in malware detection but often suffer from high false-positive rates, lower recall, and computational delays, thus demanding a more efficient and accurate system. Current techniques primarily rely on static features and simplistic learning models, leading to inadequate handling of temporal aspects and dynamic behaviors exhibited by advanced malware. These limitations compromise the detection of modern, evasive malware, and impede real-time analysis. This paper introduces a novel framework for Android malware detection that incorporates Temporal and Dynamic Behavior Analysis using Long Short-Term Memory (LSTM) networks and Attention Mechanisms. We further propose development of an efficient Grey Wolf Optimized (GWO) Decision Trees to find the most salient API call patterns associated with malwares. An Iterative Fuzzy Logic (IFL) layer is also deployed before classification to assess the "trustworthiness" of app metadata samples. For Ongoing Learning, we propose use of Deep Q-Networks (DQNs), which helps the reinforcement learning model to adapt more quickly to changes in the threat landscapes. By focusing on crucial system calls and behavioral characteristics in real-time, our model captures the nuanced temporal patterns often exhibited by advanced malwares. Empirical evaluations demonstrate remarkable improvements across multiple performance metrics. Compared to existing models, our approach enhances the precision of malware identification by 8.5 %, accuracy by 5.5 %, and recall by 4.9 %, while also achieving an 8.3 % improvement in the Area Under the Receiver Operating Characteristic Curve (AUC), with higher specificity and a 4.5 % reduction in identification delay. In malware pre-emption tasks, our model outperforms by improving precision by 4.3 %, accuracy by 3.9 %, recall by 4.9 %, AUC by 3.5 %, and increasing specificity by 2.9 %. These gains make our framework highly applicable for real-time detection systems, cloud-based security solutions, and threat intelligence services, thereby contributing to a safer Android ecosystem.

Enhancing Cybersecurity with Blockchain-based Identity Management

In the digital age, the rapid increase in cyber threats has highlighted the need for more robust and secure identity management systems. Traditional centralized identity management systems are often vulnerable to data breaches, unauthorized access, and identity theft, posing significant risks to individuals and organizations. This paper explores the potential of blockchain technology to enhance cybersecurity through decentralized identity management. By leveraging the inherent properties of blockchain—such as immutability, transparency, and decentralization—blockchain-based identity management systems offer a more secure and efficient alternative to traditional methods. The paper provides an in-depth analysis of how blockchain can be utilized to store and manage digital identities securely, reducing the risk of identity fraud and data breaches. It examines various blockchain-based identity management frameworks, highlighting their advantages and potential challenges in implementation. Furthermore, the study discusses the integration of blockchain with other emerging technologies, such as cryptographic algorithms and smart contracts, to create a more resilient cybersecurity infrastructure. Through a review of current literature and case studies, the paper demonstrates the effectiveness of blockchain in enhancing data security and privacy while promoting user control over personal information. The findings suggest that adopting blockchain-based identity management could significantly mitigate cybersecurity threats and enhance trust in digital transactions. However, the paper also acknowledges the need for addressing scalability, regulatory compliance, and interoperability issues to fully realize the potential of blockchain in cybersecurity. This research contributes to the growing body of knowledge on innovative cybersecurity solutions and provides a foundation for future studies in the field.

Digital World Threat Preparedness For Digital Transformation Acceleration Policy In Indonesia

Digital transformation is a must for Indonesia in facing global dynamics in this digital era. Through the digital transformation acceleration policy, Indonesia seeks to accelerate economic growth, improve the quality of public services, and strengthen national resilience in the digital era. The purpose of this paper underlines the importance of Indonesia's preparedness in facing the threats of the digital world, both in terms of cybersecurity, data privacy violations and other negative impacts. The method used is a qualitative research method with data reviewed through literature studies. The results show that the importance of this preparedness covers various aspects, ranging from cybersecurity infrastructure to community digital literacy to overcome challenges and risks in building a strong digital defense. In addition, there is a need for concrete steps to overcome obstacles and challenges in implementing digital transformation acceleration policies. This includes strengthening technological infrastructure, improving digital literacy, encouraging the involvement of all stakeholders, and ensuring supportive regulations and adequate privacy protection. Throughout the analysis, it is important to understand that digital transformation is not an end goal, but an ongoing journey. By identifying and addressing the challenges and risks that arise, Indonesia can optimally utilize the potential of digital transformation to achieve sustainable development and protect people's security and privacy in this digital era.

MBSE Analysis & Update of U.S. Infrastructure Data Taxonomy (IDT) Using U.S. National Critical Functions (NCFs)

AbstractIn this paper, we will demonstrate the application of Model‐Based Systems Engineering (MBSE) tools and techniques we have used to assist the U.S. Department of Homeland Security (DHS) / Cybersecurity Infrastructure Security Agency (CISA) in analysis of open‐source Infrastructure Data Taxonomy (IDT) data to analyze and contribute to the updates required for their IDT. We will show how we have used Functional Analysis, Logical Structure Taxonomies, and MBSE tools to analyze the IDT alongside the DHS National Critical Functions (NCFs) to recommend and improve these updates for their IDT. We have also proposed updates to the NCFs themselves where obvious improvements could be made. Our future recommendations are to continue this effort as a team of INCOSE Critical Infrastructure Protection and Recovery (CIPR) team members, along with other INCOSE Working Groups (WGs), and to explore other use cases based on this model data, as well as inviting other Subject Matter Experts (SMEs) to assist in the enhancements. This modeling effort demonstrated the use of these MBSE tools while working together with other organizations to enable improvements to the Critical Infrastructure (CI) space leveraging systems theory & thought leadership. We will outline further avenues for exploration using this provided MBSE capability in multiple areas that could be useful to the DHS, and will discuss a potential longer‐term engagement with DHS in an annual challenge. This challenge similar to those led by multiple other projects will include collaborations with universities and other CI / CIPR entities interested in this modeling of the CI space. Other opportunities to extend the model further include, but are not limited to security / cybersecurity, operational, and services modeling use cases, involving Industrial Control Systems (ICS) and much more. We have shown how these tools are effective and efficient for use in the water / wastewater sector, but will also yield similar benefits when applied across all identified sectors and across the international community as well (i.e., transportation, telecommunications, etc.). Interestingly we have seen in our initial analyses that the various taxonomies across sectors, subsectors, segments, subsegments, and assets vary slightly between regions and countries internationally for various reasons.

Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques.

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs' ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models' performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92% and 99.77% and overfitting values of 0.33% and 0.04% on the two datasets, respectively. The model's ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively.

Evolving Threats, Emerging Laws: Poland's 2023 Answer to the Smishing Challenge

In the face of rising cybersecurity threats like 'smishing'—SMS-based phishing attacks—this article examines how legislative efforts can effectively address these challenges. This article provides a comprehensive analysis of cybersecurity challenges, focusing on the still growing phenomenon of 'smishing', within the legislative context. In particular, it explores the legal landscape of cybercrime through the lens of Poland's recently enacted Act on Combating Abuses in Electronic Communication, as well as the European Union's Cybersecurity Strategy for the Digital Decade. The first one serves as a significant case study for examining legislative efforts aimed at mitigating cybersecurity risks in the field of electronic communications. The article describes the multi-layered, collaborative business-state approach of the Polish law, which can provide a solid framework for addressing current and future cyber security threats. The act stands as a promising tool for fortifying national cybersecurity infrastructure and could serve as a useful example for other jurisdictions grappling with similar issues. The law also engages citizens actively in its cybersecurity initiatives, promoting collective responsibility. In the broader European Union context, while the Polish Act undergoes scrutiny, this analysis also seeks to explore its alignment with the objectives outlined in the 2020′s European Union's Cybersecurity Strategy for the Digital Decade. This examination aims to evaluate the extent to which the Polish legislative framework resonates with the overarching goals set forth by the European Union, thereby contributing to a deeper understanding of the synergy between national initiatives and the broader European cybersecurity strategy context.

Railway Infrastructure Cybersecurity: An Overview

The railway infrastructure constitutes a type of operational technology (OT)-based critical infrastructure, which is expected to work 24x7, 365 days a year, and where the life expectancy of operational equipment often exceeds 30 years. In this domain, an operational anomaly compromising the OT system can cause a train accident or interrupt traffic, with potentially significant impact in terms of business as well as for passenger safety. Due to their relevance, railways are strategic assets of national interest and, consequently, targets of interest for cybercriminals and cyberwarfare activities. For instance, service interruptions may trigger ripple effects resulting in product shortages and widespread supply chain disruptions, with severe impacts for both the economy and national security. In a bid to optimise and streamline operations. the railway industry has recently started taking a series of significant steps towards digitization, with infrastructures experiencing a significant paradigm shift which, for instance, makes it possible to have centralised interlockings and Radio Block Centre (RBC) for an entire country, with geographical redundancy, ensuring the utmost availability and punctuality by moving the control logic to the cloud. Nevertheless, these developments must always be carried on within the scope of established cybersecurity standards and frameworks. This paper presents an analysis of the state of the art on railway cybersecurity, focused on the existing solutions based on the application of the CENELEC “Technical specification 50701 - Railway Application – Cybersecurity”, which is currently the latest European specification addressing railways, being designed to help suppliers, integrators, and operators to implement a cybersecurity risk assessment plan, the necessary controls, and the management of the complete system life cycle. Special attention will be paid to the conduit between the rail signal interlocking system, that controls the line signalling, and the Automatic Train Supervision (ATS) that runs in the Operational Control Centre (OCC), as this has been identified by the European Union Agency for Cybersecurity (ENISA) as one of the most critical systems identified by the operators of essential services.

Identifying Information Technology (IT) and Cybersecurity Executives’ Competencies to Support Comprehensive Cybersecurity Programs

Information Technology (IT) and cybersecurity executives play a pivotal role in shaping the cybersecurity posture of an organization. Their ability to make informed decisions, allocate resources, and communicate effectively with cybersecurity professionals is paramount. Consequently, these executives must acquire the necessary competencies that encompass cybersecurity risk management, legal and regulatory compliance, and strategic planning combined with foundational business and technical competencies. An interdisciplinary approach bridging the gap between business, technical skills and strategic decision-making is crucial to navigate the ever-evolving and complex cybersecurity challenges facing organizations today. Failure to do so may result in catastrophic consequences for both individual enterprises and society. Further, the growing frequency and sophistication of cyber threats pose significant risks to organizations and individuals alike. To effectively counter these threats, it is imperative to not only develop cybersecurity talent but also to equip IT and cybersecurity executives with essential competencies in this domain. Equally important, is to identify the specific competencies and develop an approach to train or teach them. According to Burrell, Aridi, & Nobles (2018) there is an extremely urgent need of leadership development for cybersecurity and information technology professionals to prepare these professionals with the foundational skills to excel in leadership, management, and directing an enterprise-level program. This paper underscores the critical need for a comprehensive understanding of both Information Technology (IT) and cybersecurity executive competencies and cybersecurity executive development. Integrating these two aspects is critical to improve an organization’s cybersecurity posture and ensure alignment between organizational objectives and cybersecurity strategies. The two must work in tandem to create a robust and resilient cybersecurity infrastructure. This paper provides an analysis of the current literature regarding IT/Cybersecurity roles and responsibilities, leadership competencies, and technical competencies of IT/Cybersecurity executives to identify the gaps in existing research. The authors propose a survey instrument to conduct a quantitative analysis to identify executives’ beliefs as to how important it is to possess each administrative competency. The survey is part of a future research plan to identify and evaluate administrative and technical competencies of IT/Cybersecurity executive leaders.

Interconnected and resilient: A CGE analysis of AI-driven cyberattacks in global trade.

The burgeoning interconnectedness of global trade in the digital age not only presents enticing opportunities but also harbors potent vulnerabilities of artificial intelligence (AI)-driven cyberattacks. This study explores the cascading impacts of these disruptive threats on economies, supply chains, and trade, utilizing the intricate lens of Computable General Equilibrium modeling. Through meticulously designed simulation scenarios, we illuminate the potential economic ramifications of cyberattacks, with a focus on regions heavily reliant on digital technologies and interwoven supply chains. The analysis reveals significant declines in real GDP, trade prices and volumes, and trade route disruptions across regions. Notably, economies like China, the United States, the United Kingdom, and the EU, due to their deep integration in global networks, face pronounced vulnerabilities. However, amidst this bleak landscape, hope emerges in the form of cyber resilience. The study showcases the effectiveness of proactive measures like adaptable production systems, diversified trade partners, and robust cybersecurity infrastructure in mitigating the adverse impacts of cyberattacks. Incorporating cyber resilience significantly dampens the reported negative consequences, highlighting the critical role of preparedness in combating digital warfare. This study underscores the urgent need for a global paradigm shift toward cyber resilience. Collective efforts to bolster cybersecurity infrastructures, foster international cooperation in threat intelligence, and establish open and resilient trade frameworks are crucial in navigating the treacherous labyrinth of AI-driven cyberattacks. By embracing resilience strategies and fostering global collaboration, we can pave the way for a more secure and prosperous digital future, where interconnectedness becomes a tool for progress, not a vulnerability to be exploited.

Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

Machine learning for network defense: automated DDoS detection with telegram notification integration

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.

Cybersecurity Considerations for Smart Bangladesh: Challenges and Solutions

Cybersecurity stands as a cornerstone for safeguarding the integrity, security, and prosperity of a nation in the digital era. It involves a multifaceted approach to protect interconnected digital systems, critical infrastructure, and sensitive data. In the context of a smart nation, where interconnected devices, sensors, and systems are integrated to enhance efficiency and quality of life, cybersecurity becomes even more crucial. As Bangladesh progresses towards its vision of becoming a Smart Nation, characterized by extensive integration of digital technologies into various sectors, cybersecurity emerges as a critical concern. The rapid digitization of infrastructure and services, driven by initiatives such as smart cities, e-governance, and digital healthcare, introduces new vulnerabilities and threats that must be addressed to safeguard against cyber-attacks and protect sensitive data. This paper explores the cybersecurity landscape of Bangladesh in the context of its Smart Nation aspirations, identifying key challenges and proposing solutions to mitigate risks. The cybersecurity challenges facing Smart Bangladesh initiatives are multifaceted and include inadequate cybersecurity policies, limited awareness and education, insufficient investment in cybersecurity infrastructure, and the proliferation of Internet of Things (IoT) devices with inherent vulnerabilities. These challenges expose smart systems to a range of threats, including malware and ransomware attacks, data breaches, insider threats, Distributed Denial of Service (DDoS) attacks, and IoT vulnerabilities. To address these challenges, stakeholders must adopt a proactive and multi-layered approach to cybersecurity. Recommendations include the development of comprehensive cybersecurity policies, enhanced public awareness and education campaigns, investment in cybersecurity infrastructure, implementation of secure-by-design principles in smart infrastructure development, and fostering public-private partnerships to share threat intelligence and resources. By prioritizing cybersecurity considerations and implementing robust cybersecurity measures, Bangladesh can build a resilient and secure digital ecosystem that supports its Smart Nation goals. However, addressing cybersecurity challenges requires coordinated efforts from government agencies, private sector organizations, academia, and civil society to create a cyber-resilient environment conducive to sustainable development and innovation. The author found this area of utmost interest and decided to delve deep into the aspects related to cybersecurity, in order to develop a well-designed journey towards a digitally transformed Bangladesh.

Integrated cybersecurity for metaverse systems operating with artificial intelligence, blockchains, and cloud computing

In the ever-evolving realm of cybersecurity, the increasing integration of Metaverse systems with cutting-edge technologies such as Artificial Intelligence (AI), Blockchain, and Cloud Computing presents a host of new opportunities alongside significant challenges. This article employs a methodological approach that combines an extensive literature review with focused case study analyses to examine the changing cybersecurity landscape within these intersecting domains. The emphasis is particularly on the Metaverse, exploring its current state of cybersecurity, potential future developments, and the influential roles of AI, blockchain, and cloud technologies. Our thorough investigation assesses a range of cybersecurity standards and frameworks to determine their effectiveness in managing the risks associated with these emerging technologies. Special focus is directed towards the rapidly evolving digital economy of the Metaverse, investigating how AI and blockchain can enhance its cybersecurity infrastructure whilst acknowledging the complexities introduced by cloud computing. The results highlight significant gaps in existing standards and a clear necessity for regulatory advancements, particularly concerning blockchain’s capability for self-governance and the early-stage development of the Metaverse. The article underscores the need for proactive regulatory involvement, stressing the importance of cybersecurity experts and policymakers adapting and preparing for the swift advancement of these technologies. Ultimately, this study offers a comprehensive overview of the current scenario, foresees future challenges, and suggests strategic directions for integrated cybersecurity within Metaverse systems utilising AI, blockchain, and cloud computing.