Cybersecurity Challenges Research Articles

An Efficient Network Intrusion Detection and Classification System using Machine Learning

In today's digital landscape, network security is of paramount importance, with intrusion detection systems (IDS) playing a crucial role in protecting sensitive data from malicious attacks. Traditional IDS, often reliant on signature-based methods, struggle with high false positive rates, difficulty in adapting to novel threats, and significant computational demands. This paper explores the development of an efficient network intrusion detection and classification system utilizing machine learning techniques to address these challenges. By leveraging datasets such as NSL-KDD and UNSW-NB15, our study employs a combination of supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, alongside comprehensive data preprocessing and feature engineering strategies. The evaluation of our models through metrics like accuracy, precision, recall, and ROC-AUC demonstrates a marked improvement in detection capabilities and computational efficiency. Our findings suggest that machine learning-based IDS can significantly enhance network security by reducing false positives and adapting to emerging threats more effectively than traditional systems. This research not only underscores the potential of advanced machine learning techniques in IDS but also provides a robust framework for future developments in the field. In the rapidly evolving landscape of cybersecurity, effective network intrusion detection and classification systems are critical for safeguarding sensitive data and maintaining operational integrity. This paper presents a novel approach utilizing machine learning techniques to enhance the efficiency and accuracy of intrusion detection systems (IDS). By employing a combination of supervised and unsupervised learning algorithms, our system can identify and classify both known and unknown threats in real-time. We leverage advanced feature selection methods to optimize the performance of our models, ensuring high detection rates with minimal false positives. Our experimental results, validated on benchmark datasets, demonstrate significant improvements in detection accuracy and processing speed compared to traditional IDS solutions. The proposed system not only strengthens network defenses but also provides a scalable and adaptive framework for future cybersecurity challenges..

Neobanks in India: Innovation, Challenges and Opportunities in the Banking Industry

Neobanks, also known as digital banks, is a new form of financial institutions that operate exclusively online, without the presence of physical branches. These banks leverage advanced technology for offering innovative and customer-centric banking services. This paper provides a comprehensive review of the evolution, growth, and current landscape of neobanks, highlighting their key differences compared to traditional banks and some of the specific challenges they face in India. The study also assesses the scope and future prospects of neobanks in India, analyzing opportunities for growth amidst regulatory challenges and market situations. This paper also focuses on the global performance of neobanks, particularly in Europe and Korea, and draw comparisons with the Indian context. This paper aims to explore the transformative potential of neobanks in the financial sector. The findings suggest that despite significant regulatory and cybersecurity challenges, neobanks have the potential to redefine the banking experience by offering seamless, user-friendly services tailored to the modern, tech-savvy consumer.

The Growing Cybersecurity Crisis in Healthcare: A Call to Action

Electronic healthcare technology is pervasive throughout the globe, and it affords vast opportunities to enhance clinical outcomes, as well as for the transformation of models of care. Concerns are, however, growing, related to healthcare data and device security. Increased connectivity to legacy computer networks brought cybersecurity vulnerabilities for medical devices. Healthcare represents an attractive target for cybercrime because healthcare data is precious. This sector in present times is full of unique cybersecurity challenges, especially for susceptible kinds of patient information at stake. Second, many legacy systems will be prevalent—not adding more to that—with the changing face of cyber threats. Cybersecurity breaches have comprised stealing health information and focused ransomware attacks on hospitals; this could mean as vivid an attack as on implanted medical devices. This only points to the fact that ransomware attacks and other kinds of cyber-attacks against hospitals and other medical facilities are gaining ground; there is every reason to get alarmed and put in place stricter cybersecurity measures. An excellent healthcare cybersecurity strategy, therefore, has to consider access control, intrusion detection systems, encryption techniques, and periodic security testing. Data breaches and cyber-attacks are forcing any healthcare provider to invest in new state-of-the-art technologies related to keeping pace with trends regarding cybersecurity. The dangers that can be caused by cyber-attack include a considerable diminution in patient trust, potential health system collapse, human life threats, etc. On the whole, cybersecurity is strenuously linked with the question of patient safety.

Uncovering Multi-step Attacks with Threat Knowledge Graph Reasoning

The rapid advancement of information technologies has significantly intensified the focus on cyberspace security across various sectors. In this evolving landscape, attackers deploy many of techniques- including exploits, weakness identification, and complex multi-step attacks- to gain unauthorized access to systems. Conversely, defenders harness insights from a variety of sources to pinpoint potential threats. Prominent public cybersecurity databases such as the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK), Common Attack Pattern Enumeration and Classification (CAPEC), Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Platform Enumeration (CPE) provide extensive data on security entities and their interrelations, playing a pivotal role in enriching the understanding of cybersecurity challenges and assisting in comprehensive defensive analyses. However, the semantic cross-analysis of these databases, crucial for identifying obscure threat patterns, remains underexploited. In this study, we amalgamate data from these disparate sources into a cohesive threat knowledge graph and introduce a novel knowledge representation learning approach, A4CKGE (ATT&CK-CAPEC-CWE-CVE-CPE Knowledge Graph Embedding). This method utilizes advanced structural and textual analytics to predict interactions among security entities such as products, vulnerabilities, weaknesses, and multi-step attack sequences, employing complex attack templates generated through a Large Language Model (LLM). Our extensive experiments demonstrate that this approach significantly outperforms existing state-of-the-art methods in effectively predicting these relationships. The findings validate the efficacy of our threat knowledge graph in unveiling hidden connections, thereby highlighting its potential to strengthen cybersecurity defenses substantially.

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

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

Assessing the Impact of Technology on the Efficiency and Effectiveness of Investment Banking Services

This paper evaluates the effect of innovation on the effectiveness and adequacy of venture managing an account administrations. It reflects on the way digital innovations such as artificial intelligence, blockchain, and data management systems have transformed such missions as securities underwriting, mergers and acquisitions, and client advisory services. By combining these technologies, investment banks optimize their processes, improve precision, and bring in enhanced customer experiences. Deliberating through critical review of literature and specific cases from the industry, this paper explores how artificial intelligence changes the decision-making processes, automates trading strategies, and enhances risk management, while blockchain changes back-office operations by making transactions faster and more secure. Advanced data management tools equally provide improved insight into the enhanced client services and decisions in investments for the banks. Although this brings cost-cutting and operational efficiency, these technologies pose challenges in cybersecurity and regulatory compliance due to the expansion of digital platforms, which provide increased opportunities for cyber-attacks and a need for strict adherence to ever-changing regulations. The paper provides a panoramic view of the current and future role of technology in evolving investment banking.

Cybersecurity Transformation: Cyber-Resilient IT Project Management Framework

In response to the escalating threats of cybersecurity attacks and breaches, ensuring the development and deployment of secure IT products has become paramount for organizations in their cybersecurity transformation. This work emphasizes the critical need for a comprehensive and secure IT project management life cycle that safeguards products from their initial development stages through decommissioning. The primary objective is to seamlessly integrate security considerations into every facet of IT project management life cycles. This work embraces a cyber-resilient IT project management framework and advocates the inclusion of cybersecurity measures in IT projects and their strategic, organized, continuous, and systematic integration throughout the entire product life cycle. It introduces a pioneering framework that harmonizes the cybersecurity risk management process with the IT project management life cycle. This framework delineates a methodical sequence of steps, each encompassing a distinct set of activities. The effectiveness and practical applicability of the proposed framework were validated through a comprehensive case study focused on the Personal Health Record (PHR) system. The PHR case study served as a real-world scenario to assess the framework’s ability to address cybersecurity challenges in a specific domain. The results of the experiment demonstrated the framework’s efficacy in enhancing the security posture of IT projects, showcasing its adaptability and scalability across diverse applications.

Cybersecurity Challenges in the Context of Digital Transformation

The present article examines the cybersecurity challenges arising from digital transformation, highlighting key issues – data breaches, ransomware, and Internet of Things (IoT) vulnerabilities. It discusses the evolving tactics of cyber adversaries and the importance of artificial intelligence (AI) in improving cybersecurity measures while addressing ethical issues and regulatory efforts. It highlights the need for a balanced approach to cybersecurity and offers recommendations for a more sustainable stance on the issue. Overall, valuable information is provided for cybersecurity professionals, policymakers, and researchers navigating the complexities of digital transformation and cybersecurity, as well as for each user.

Machine Learning Model for Content Protection from Cyber security Threats

Cyber security is the shield that protects our digital world from unauthorized access, data breaches, and evolving threats. The former views human involvement as an extra dimension and a possible target, while the second considers it as part of the security process. However, because it centers on the ethical aspect of society as a whole, this kind of cyber security conversation has significant implications. Numerous frameworks and strategies have been proposed to handle the cyber security challenge. The ideas of cyber security are also introduced, along with information on individuals, frameworks, and protecting personal data on computers. This paper aims to emphasize the effective approach for Machine Learning Model for content from Cyber Security threats. The results show that, in this scenario, the Support Vector Machine (SVM) classifier performs better than other algorithms.

Cybersecurity Challenges and Invasion of Privacy: An In-Depth Analysis

Cybersecurity Challenges and Invasion of Privacy: An In-Depth Analysis

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach

Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats.

A Study on Cybersecurity Challenges and Strategies in a Digitally Transformed Business Environment

In this era of digital transformation businesses are facing a rapidly changing cybersecurity landscape with new technologies, expanded attack surfaces and advanced threats. This article looks at the many cybersecurity challenges that arise from integrating advanced technologies like cloud, IoT and AI into business. The challenges are expanded attack surfaces, system complexity, data privacy and regulatory compliance and advanced threats. The article looks at the solutions to these challenges including Zero Trust architecture, layered security, advanced threat detection, incident response and security awareness training. plus real world case studies of how these solutions are being used and what’s coming next with emerging technologies and regulations. By addressing these challenges with a comprehensive approach businesses can strengthen their cybersecurity, protect their data and be resilient in a digital world. KEYWORDS Cybersecurity Challenges ,Digital Transformation, Cyber Threats ,IT Infrastructure ,Zero Trust Security ,Threat Detection ,Incident Response, Data Protection ,Regulatory Compliance Employee Training, Advanced Security Technologies, Cloud Security, Network Security

Cybersecurity in Learning Systems: Data protection and privacy in educational information systems and digital learning environments

This research addresses the growing cybersecurity challenges within educational information systems and digital learning platforms, focusing on the protection of sensitive data and user privacy. The objective is to identify prevalent cybersecurity threats in these environments and propose effective solutions to mitigate them. A mixed-method approach is employed, combining a comprehensive literature review with a survey distributed to IT professionals and educators working in digital learning environments. The findings highlight the increasing sophistication of cyberattacks, including data breaches, phishing, and malware, which compromise the integrity and security of educational data. Moreover, the study reveals a gap in the implementation of robust cybersecurity policies, especially in underfunded educational institutions. The proposed solutions emphasize the integration of advanced encryption methods, multifactor authentication, and regular cybersecurity training for all stakeholders. In conclusion, this research underscores the importance of developing a resilient cybersecurity framework tailored to educational systems, ensuring the protection of both institutional data and the privacy of users, thereby enhancing trust and security in digital learning environments.

Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.

Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities

Digital Twin (DT) technology has become a cornerstone of modern smart city infrastructure, providing real-time insights and operational efficiencies by creating a virtual replica of physical systems such as traffic networks, energy grids, and public services. While these advancements enable optimized urban management and improved decision-making, they also present new cybersecurity challenges that can potentially jeopardize the safety and reliability of critical infrastructures. This study addresses the cybersecurity risks associated with Digital Twin deployments in smart cities, focusing on threats such as unauthorized access, data manipulation, and hijacking of the DT models, which could result in service disruptions and compromise public safety. The research employs a comprehensive risk assessment methodology based on the NIST Cybersecurity Framework, where potential risks are identified, evaluated, and prioritized according to their severity and likelihood of occurrence. To mitigate these risks, a multi-layered security framework was developed, incorporating encryption mechanisms, robust access control, and an Artificial Immune System (AIS)-based anomaly detection model. The framework was tested through a simulated case study on a smart transportation system within a smart city environment, demonstrating its effectiveness in preventing data tampering and detecting unauthorized access. The results indicate that the proposed security model reduced data manipulation incidents by 35%, decreased response times for threat detection by 25%, and improved overall system resilience by 40%. These findings underscore the critical need for proactive cybersecurity strategies in ensuring the secure and resilient deployment of Digital Twin technologies in smart cities. The study concludes by emphasizing the importance of continuous security monitoring and adaptive threat management to safeguard smart city ecosystems from evolving cyber threats

Cyber Security Awareness Model Based on NIST (National Institute of Standards and Technology) for Secondary School Students in Malaysia

As cybersecurity issues surge, it highlights the pressing need for increased awareness. In our digitally interconnected world, where information technology underpins the lives of individuals, businesses, and even national security, the threat of cyberattacks looms large. To enhance national cybersecurity preparedness, there is a critical call to action: we must promote public awareness of cybersecurity issues and bolster the ranks of trained cybersecurity professionals. This research introduces a novel cybersecurity awareness model based on the National Institute of Standards and Technology (NIST). Its primary goal is to make NIST accessible to secondary school students pursuing ICT courses and their invaluable resource, the school counselling units. The model aims to instil cybersecurity awareness in secondary school students and guide them in choosing the most suitable higher education paths, particularly in the ICT and cybersecurity fields. The study comprises three phases. In the first phase, a systematic literature review (SLR) examines globally applicable cybersecurity education models. The second phase investigates the implementation of cybersecurity education for secondary school students in Malaysia. This includes interviews with school counselling units responsible for guiding students toward higher education and surveys of students in grades four and five enrolled in ICT courses. The third and pivotal phase focuses on developing the proposed model. It involves analysing and implementing the results of the SLR, mapping them to the NIST framework, and incorporating themes from interviews and surveys. As a tangible representation of the model, a mobile application prototype has been developed. The Cybersecurity Awareness Model empowers school counselling units to seamlessly integrate career exploration with career decision-making for secondary students at any stage of their development. Moreover, it offers a means to create strategies for after-school programs, particularly in the dynamic field of cybersecurity. This model is expected to guide students in exploring academic majors and careers, preparing for higher education post-high school, initiating internships or job searches, and adapting to a world in constant flux. In doing so, it not only empowers individuals but also strengthens the nation's resilience in the face of cybersecurity challenges.

Pemanfaatan Teknologi Informasi dalam Edukasi Literasi Digital untuk Peningkatan Keamanan Data dan Pencegahan Kejahatan Siber di Masyarakat Rawang Panca Arga

The utilization of information technology in digital literacy education has become increasingly important due to the growing threat of cybercrime. This research aims to analyze the impact of digital literacy education on improving data security and preventing cybercrime within the community. Using a qualitative approach, this study identifies the role of information technology in raising public awareness and understanding of the importance of protecting personal data online. The findings reveal that digital literacy education, supported by information technology, can significantly reduce the risk of cybercrime. Communities that are educated in digital literacy are better equipped to face cybersecurity challenges and safeguard the integrity of their data. This education also provides practical guidelines for using digital devices safely and ethically.

Cyber Security Challenges in the Transportation Industry: A Comprehensive Analysis and Recommendations

Cyber Security Challenges in the Transportation Industry: A Comprehensive Analysis and Recommendations

Addressing cybersecurity challenges in robotics: A comprehensive overview

As robotics technology becomes increasingly integrated into various sectors, ensuring the cybersecurity of robotic systems is paramount. This article provides an in-depth exploration of the cybersecurity challenges confronting robotics and offers strategies to address these concerns. With the growing connectivity and networking capabilities of robots, vulnerabilities such as unauthorized access, data breaches, and network attacks are significant threats [1]. Protecting sensitive data collected and processed by robots is crucial to preserving privacy and trust. Remote access features, while enhancing operational flexibility, also pose security risks if not adequately secured. Weak authentication mechanisms and insecure interfaces could allow malicious actors to compromise robot functionality. Furthermore, robots are susceptible to malware and cyber-attacks, including viruses, worms, and ransomware. To mitigate these risks, a comprehensive approach is necessary, incorporating secure design principles, robust authentication mechanisms, encryption techniques, and cybersecurity training. Collaboration among industry stakeholders, researchers, policymakers, and cybersecurity experts is essential to develop resilient robotic systems capable of withstanding evolving cyber threats. This article underscores the importance of addressing cybersecurity challenges in robotics to ensure the safety and security of robotic deployments across diverse domains. As robotics technology evolves and becomes integral across various sectors, prioritizing cybersecurity [2] is crucial to protect these systems from unauthorized access, data breaches, and network attacks. The interconnected nature and remote access features of robots pose significant vulnerabilities. Comprehensive measures, including secure design, encryption, and cybersecurity training, are essential. Collaboration among industry stakeholders, researchers, policymakers, and cybersecurity experts is vital for developing resilient robotic systems. This article highlights the urgent need to address cybersecurity challenges to ensure the safety and integrity of robotic deployments.