Future Cyber Research Articles

THE ROLE OF AI IN ENHANCING IDENTITY AND ACCESS MANAGEMENT SYSTEMS

In the modern digital world, safeguarding sensitive data and managing access to critical systems are essential for organizations. Identity and Access Management (IAM) is a key framework for controlling access to systems and data, ensuring only authorized users can gain access. Traditionally relying on static methods like passwords, IAM systems are now facing challenges due to the complexity of cyber threats and the increasing number of users and devices. To address these issues, AI is transforming IAM by improving user authentication, detecting security anomalies, and refining permission management. AI contributes to user authentication through biometric technologies like facial recognition, fingerprint scanning, and voice recognition, reducing vulnerabilities from traditional methods. Machine learning also enhances authentication by continuously analyzing user behavior, adapting systems to recognize legitimate users more accurately. Additionally, AI plays a vital role in anomaly detection by analyzing user activity data across various platforms and identifying unusual patterns that indicate potential threats. AI's impact extends to dynamic and context-aware permission management, offering real-time adjustments based on factors such as user role and location. Furthermore, AI supports continuous risk assessment and regulatory compliance by monitoring user activities and ensuring proper access controls. The integration of AI in IAM also strengthens cloud security, as seen in the research of Muppa (2022), Mandru (2022), Oduri (2019), Mohammed (2021), Ramakrishnan (2021), and Subburaman (2022), who explore how AI helps mitigate emerging threats, optimize authentication, and improve access control in cloud environments. Ultimately, AI in IAM offers a more adaptive, resilient, and precise solution to evolving security challenges. Organizations adopting AI-powered IAM systems will be better equipped to face future cyber threats, ensuring both data protection and operational continuity.

A stochastic optimisation model to support cybersecurity within the UK national health service

Over the past decade, the adoption of new digital technologies in healthcare has surged, significantly enhancing care delivery and accessibility. However, this digital transformation has been accompanied by a sharp increase in cyber-attacks, posing severe risks to hospital functionality and patient safety. To address the challenge of planning for uncertain future cyber incidents, we propose a two-stage stochastic model designed to bolster the cyber resilience of healthcare providers by selecting optimal countermeasures in preparation for upcoming cyber incidents. Numerical tests demonstrate the model’s effectiveness, with the Value of the Stochastic Solution showing a 21% improvement over a deterministic approach. To be optimally equipped even for low-probability high-impact attacks we incorporate the risk measure Conditional Value-at-Risk. The corresponding countermeasure solution led to 44% fewer rejected patients in a worst-case scenario. The robustness of the proposed solution is underscored by its consistent performance across various scenarios, budget levels, and risk preferences, making it a reliable tool for enhancing cybersecurity in healthcare. These results highlight the importance of tailored, robust cybersecurity strategies in healthcare, ensuring preparedness for a wide range of potential threats.

Assessing the Effects of Cyber Attacks on Financial Markets

Cyber-attacks on financial markets have emerged as a significant threat to global economic stability, disrupting market operations, compromising sensitive data, and undermining investor confidence. This study explores the multifaceted impact of cyber-attacks on financial markets, examining both the immediate and long-term effects on market performance, regulatory response, and overall market resilience. By analyzing recent incidents, this research highlights how cyber-attacks influence stock prices, trading volumes, and market volatility, shedding light on the vulnerabilities within financial systems. Findings suggest that cyber threats not only impact individual institutions but also ripple through interconnected markets, amplifying financial risk on a broader scale. The study concludes with recommendations for strengthening cybersecurity measures in financial markets to enhance resilience and mitigate potential losses from future cyber incidents.

Importance of Cybersecurity/The Relevance of Cybersecurity to Diabetes Devices: An Update from Diabetes Technology Society.

As medical devices become more integrated with wireless technologies, the risks of cyberattacks and data breaches increase, making stringent cybersecurity measures essential. The implementation of rigorous cybersecurity standards is essential for enhancing the cybersecurity of devices. This article examines the evolving cyber threats faced by the medical technology industry, the role of IEEE 2621 in providing comprehensive security benchmarks for medical devices, and the need for continuous risk assessments and adherence to regulatory standards to mitigate future cyber risks. Adherence to cybersecurity standards establishes ensures the effective protection of sensitive data and critical infrastructure.

Machine Learning Approach for Classification of Cyber Threats Actors in Web Region

In the interconnected scape of today's internet, the dark web emerges as a concealed point, covering a myriad of illicit activities that pose substantial cybersecurity risks. This study investigates the attribution of threats within the dark web environment, leveraging on a machine learning approach to bridge the gap between technical indicators and linguistic and behavioral insights. Through a comprehensive methodology involving web crawling and data gathering, a dataset encompassing key variables such as attack motivation, method, web part, and threat actor was gathered. Principal Component Analysis was employed for feature selection, followed by the application of Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), and CatBoost algorithms for classification. Performance evaluation metrics including precision, recall, and F1-score were utilized to assess the efficacy of each algorithm. Results indicate a notable prevalence of cybercrimes within the dark web, underscoring the necessity for enhanced cybersecurity strategies tailored to address its unique challenges. Furthermore, the comparative analysis demonstrates varying performance levels among the machine learning algorithms, with Multinomial Naive Bayes exhibiting the highest accuracy. This research contributes to advancing threat attribution techniques in the dark web, ultimately aiming to bolster cybersecurity defenses and mitigate future cyber threats.

Analyzing Cryptographic Techniques and Machine Learning Algorithms for Crime Prediction

This report discusses cryptography techniques. Network security is defined as "keeping information hidden and secure from unauthorized users," whereas cryptography is defined as "the science of data protection." The Fundamental Requirements for Data Transmission are addressed in this work and as well as security attacks such as Data Transmission Interruption, Interception, and Modification. The Cryptographic Framework is explained using a generalized function, in which data is encrypted and decrypted using techniques such as the RSA algorithm, Hash Functions, and other cryptographic algorithms.SVM algorithms to analysis and predict the future cyber crime. This report introduces Cryptography Techniques. Cryptography is “The science of protecting data” & Network Security “keeping information private and Secure from unauthorized Users”. This paper gives the Fundamental Requirements for the Data Transmission, the security attacks like Interruption, Interception and Modification of the data Transmission. Keywords—cryptography, cybersecurity, machine learning, cybercrime prediction

Strengthening Healthcare Data Security with Ai-Powered Threat Detection

As the healthcare industry undergoes rapid digital transformation, the need for robust cybersecurity measures has never been more critical. AI-driven solutions, such as Machine Learning (ML) and anomaly detection, are proving to be pivotal in securing healthcare data. These technologies enable healthcare organizations to identify cyber threats proactively, automate incident response, and enhance data security. Furthermore, AI's ability to provide continuous network monitoring, predictive analytics, and real-time anomaly detection offers healthcare providers the tools needed to mitigate risks before they escalate. This research highlights the key applications of AI in healthcare data security, including its effectiveness in vulnerability management and regulatory compliance. It also delves into the ethical and operational challenges of integrating AI-driven threat detection systems into healthcare settings, including concerns related to bias in AI models, regulatory hurdles, and the complexity of AI system integration into existing healthcare infrastructures. In a 2023 study, Accenture reported that AI-based cybersecurity systems reduced detection and response time by up to 60%, illustrating how AI accelerates response to potential data breaches​. Similarly, HIMSS noted that the risk of data breaches in healthcare could be halved by AI technologies that continuously monitor and analyze data​. These findings emphasize AI's role in minimizing the delays and errors typically associated with human-driven responses​. AI techniques such as supervised, unsupervised, and semi-supervised learning play a crucial role in anomaly detection. These models identify irregular patterns in healthcare systems, flagging potential threats even when subtle​. For example, deep learning approaches, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can uncover outliers in healthcare data, enhancing system security​. The ability of AI to continuously learn from previous attacks ensures that systems evolve with emerging threats, further solidifying its place in healthcare cybersecurity​. Additionally, AI aids in predictive analytics, which can forecast future cyber threats based on historical data, allowing healthcare providers to address vulnerabilities proactively​. By leveraging such capabilities, AI not only safeguards sensitive patient data but also enables healthcare institutions to stay compliant with stringent regulatory standards like HIPAA and GDPR​. However, AI implementation in healthcare security comes with its own challenges. Integrating AI into legacy systems requires substantial infrastructure upgrades, and the complexity of dynamic encryption can strain system resources​. Furthermore, the ethical concerns surrounding AI, such as the transparency of decision-making processes and potential bias in AI algorithms, must be carefully managed to ensure equitable and effective security solutions​

Cyber Defense Using Cyber Threat Intelligence to Anticipate and Avoid Future Cyber Attacks

Research objective: This study evaluates the efficiency of cyber threat intelligence (CTI) for predicting and mitigating cyber threats, which is important in business today. Organisations endure cyber assaults and fight cyber crimes, which threaten commercial exposure. CTI is a proactive approach to these risks since it gives the finest methods and relevant information on potential cyber crimes. Research significance: This review article analyses the literature in the context to find gaps and include ethical research practices. This systematic study may determine how threat intelligence improves cyber security knowledge and eliminates cyber threats by examining assessable information. It may guide the development of new threat intelligence-influencing methods and frameworks. Methodology: Through a qualitative approach based on the systematic review, the study goals were evaluated, analysed, and explained. PRISMA chart was also used to describe exclusion and inclusion study criteria to ensure correct data gathering. The results were presented in a thematic analysis while examining article dependability, quality, and validity. The author used inductive research to reach the primary study conclusions. Using observations or data, inductive inquiry creates hypotheses or generalisations. Research findings: Threat intelligence may considerably increase an organisation's ability to anticipate and prevent cyber threats. The literature emphasised that threat information improves incident response, identifies new threats, and strengthens cyber security. Organisations should continually train and educate cyber security personnel to increase threat intelligence utilisation. Instruct incident response, threat intelligence analysis, and emerging threat trends.CTI helps a company grow smoothly and achieve its goals.

The Role of PASTA in Addressing Future Trends in Regulatory Compliance: Emerging Cyber Threats

The evolving landscape of cyber threats necessitates an adaptive approach to threat modeling and regulatory compliance. This paper explores the integration of the PASTA (Process for Attack Simulation and Threat Analysis) framework with emerging regulatory trends to address future cyber threats. The study examines how PASTA can be aligned with regulatory frameworks such as PCI DSS, HIPAA, GDPR, and CCPA, to enhance cybersecurity resilience and compliance. By analyzing the application of PASTA to Advanced Persistent Threats (APTs) and ransomware, the paper demonstrates how this structured methodology can help organizations effectively model, simulate, and mitigate sophisticated attacks. The integration of PASTA with regulatory requirements provides a comprehensive approach to managing cyber risks, ensuring robust protection against both current and emerging threats.

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.

INVESTIGATING VIRUS SPREAD ANALYSIS IN COMPUTER NETWORKS WITH ATANGANA–BALEANU FRACTIONAL DERIVATIVE MODELS

This paper proposes a mathematical model to investigate the dynamic behaviors of a modified computer virus model using the Atangana–Baleanu fractional derivative in the Caputo sense, aiming to elucidate the connection between its parameters and network attributes. By introducing a relatively new numerical method, we address the memory-dependent and nonlocal features of the system. The existence and uniqueness of the model’s solution are confirmed. To explore solution trajectories and assess the impact of various input factors on computer virus dynamics, we employ an efficient numerical technique. Our simulations provide insights into the consequences of fractional order, anti-virus measures, asymptotic fraction, damage rate, and removal rate in the system. These findings illuminate the relationships between model parameters, facilitating the design of networks that minimize the risk of virus outbreaks and prevent future cyber threats. By identifying critical factors involved in the progression of viruses, these results enable the development of more effective defense mechanisms.

Unveiling the Depths of Phishing: Understanding Tactics, Impacts, and Countermeasures

Phishing attacks have emerged as a pervasive threat in the cybersecurity landscape, exploiting human vulnerabilities to compromise sensitive information and perpetrate cybercrime. This paper provides a comprehensive examination of phishing attacks, encompassing their methods, prevalence, and impact on individuals and organizations. Through an in-depth literature review, we delve into the various types of phishing attacks, including email phishing, spear phishing, and pharming, elucidating their distinct characteristics and methodologies. We highlight the significant financial and reputational costs incurred by victims of phishing attacks across different sectors. Despite advancements in cybersecurity technologies and awareness programs, phishing attacks persist as a prominent attack vector due to their adaptability and sophistication. Current mitigation strategies, such as email filtering, user education, and multi-factor authentication, offer some defense against phishing attacks but are not without limitations. Building upon this foundation, we discuss emerging trends in phishing attack vectors and explore the potential of advanced technologies, such as machine learning, artificial intelligence, and blockchain, to enhance phishing detection and prevention capabilities. Furthermore, we underscore the importance of collaboration between industry stakeholders, government agencies, and cybersecurity researchers in combating phishing attacks and fortifying cyber resilience. By fostering greater awareness and cooperation within the cybersecurity community, we can develop more effective defenses against phishing attacks and mitigate the impact of future cyber threats.

Enhancing IoT Security through Machine Learning-Driven Anomaly Detection

This is study emphasizes the growing cybersecurity situations arising from the increasing use of Internet of Things (IoT) devices. Paying the main attention to the development of IoT security, the work here deploys the machine learning-based anomaly detection and adaptive defense mechanisms as proactive methods to counteract existing plus future cyber threat sources. The visual serves to expound the rapid development of the Internet of Things, and it also highlights the importance of infrastructures with robust safety features to secure the connected devices. IoT security statement brings out the hidden threat and vulnerabilities of the IoT, in this context advanced security measures are for the rescue. The objectives concentrate on improving security of IoT via machine learning detection of anomalies, and bring introduction of defense mechanisms that are adaptive. We specify the data sources, preprocessing tasks, and Random Forest, Decision Tree, SVM, and Gradient Boosting algorithms selected for anomaly detection in the methodology section. The abnormity negotiation function and the self-adaptive defense procedures are combined in order to strengthen the information technology ecosystems which are capable of dynamic simplification. The results and discussion part hotelates the effectiveness of machine learning models selected, and indicates about accuracy, precision, and recall metrics. To state in the most significant matter, Gradient Boosting brings the greater precision of 89.34%. Table 3 below indicates the various models' effectiveness. It is proven that Gradient Boosting is the most powerful model among all. The discourse unfolds with account of the results, acknowledgment of the limitations, and discussion crucial obstacles encountered in the realization of the research. The conclusion reaffirms the importance of machine learning in IoT security implementation, thus building a robust system that can evolve to fight the ever-emerging cyber-attacks, keeping up with the progressive direction for securing IoT through the connected world.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Deceptive Patch Solutions for Protecting Industrial Control Systems Based on Discovered Vulnerabilities

An increase has been observed in concerns about cyber security threats in smart energy management on a global scale. Industrial Control Systems, or simply ICSs, are frequently present in industries and essential infrastructures, e.g., water treatment facilities, nuclear and thermal plants, heavy industries, power production, and distribution systems. ICS devices are high-risk targets for attacks and exploitation with significant security difficulties for ICS vendors and asset owners. Like many consumer electronics, industrial systems are susceptible to a bevy of vulnerabilities that hackers can exploit to launch cyber attacks. Extensive use of ICSs in Critical Infrastructures (CI) increases the vulnerability of CI to cyber attacks and makes their protection a critical subject. This study first contributes to a novel line of research considering how deception can be used by defenders in strategic terms with the objective of introducing uncertainty into an adversary’s perception of a system patch management process in order to protect ICSs. Thus, we mention the advantages of patch models to improve the vulnerabilities of ICSs. We explore deceptive patch management models for the purpose of providing better insight into developing future cyber security techniques for ICS attacks. We propose deceptive patch management solutions as case studies for common ICS attacks.

Machine Learning Ensures Quantum-Safe Blockchain Availability

ABSTRACT This study explores quantum computing and blockchain, focusing on quantum-safe algorithms. As quantum computing progresses, it threatens blockchain cryptography, necessitating quantum-resistant/safe algorithms. Using statistical analysis and simulations to address future cyber threats, we analyze quantum-safe cryptography in blockchains. We pioneer machine learning models to assess quantum-safe algorithm performance across encryption, key generation, signatures, speed, scalability, energy efficiency, and security. Our analysis, referencing Ethereum’s Ether values, pinpoints areas for quantum-safe cryptography improvements, addressing limitations and proposing solutions to strengthen security and efficiency. In experiments, we observed a 15% increase in encryption strength, 10% enhanced key generation efficiency, and improved reliability parameters. These findings stress the importance of machine learning in quantum-safe cryptography for widespread adoption and long-term security against quantum threats. In conclusion, our research paves the way for the integration of quantum-safe algorithms, ensuring the resilience of blockchain systems in the face of quantum advancements.

Cyber Defense Using Cyber Threat Intelligence to Anticipate and Avoid Future Cyber Attacks

Cyber Defense Using Cyber Threat Intelligence to Anticipate and Avoid Future Cyber Attacks

Cybersecurity Improvement Design in Critical Infrastructure PT XYZ Case Study

Prevention of cyber attacks is an important factor that organizations need to do to support maximum business process continuity. One of the important factors needed is the protection of data and information assets by the critical infrastructure owned. This paper discusses increasing the capacity of critical infrastructure at XYZ organization. The NIST CSF framework is a benchmark assessment tool for critical infrastructure capacity at PT XYZ. The results obtained from the assessment are used to make recommendations for controls related to critical infrastructure. The risk assessment found 107 risk scenarios that led to 11 recommendations for critical infrastructure improvement areas. Prioritization of the resulting recommendations is expected to increase the resilience and capacity of critical infrastructure at PT XYZ in the face of current and future cyber threats.