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Literature Review: Cybersecurity

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Introduction

The population globally has become more dependent on electronic systems and devices; cybersecurity has become a significant field as cases of cybercrimes are increasingly being reported. Scholars have delved into different aspects of cybersecurity, such as threat identification, vulnerability assessment, and users’ attitudes, to enhance cybersecurity. This literature review aims to identify significant arguments for cybersecurity, running issues, and viewpoints that accentuate recognized currents of thought in the presented field. Conducted by reviewing the current literature, this paper aims to offer a synthesis of the current measures for maintaining cybersecurity combined with the challenges it entails in order to open up the discourse on effective threat identification, efficient frameworks for risk management, and the part played by users in improving organizational security.

Definitions

Cybersecurity: Protecting systems, networks, and programs from digital attacks.
Cyber Threat: Any potential malicious attempt to damage or disrupt a computer network or system.
Risk Management: Identifying, assessing, and controlling threats to an organization's capital and earnings.
User Behavior: Actions and behaviors of individuals using information systems can impact the security of those systems.

Threat Detection

Threat detection in the context of cybersecurity is concerned with identifying and preventing various cyber threats employing innovative methods. The review praised machine learning algorithms for their applicability to identifying malware and phishing attacks due to their capability to learn from new threats (Okoli et al., 2024). Sophisticated solutions like machine learning improve the models’ ability to identify program flaws by analyzing the patterns and deviations in the vast data sets. The anomaly detection methods, which are aimed at detecting unknown threats by examining the differences in the behaviour from the normal one, are also essential. It is also noteworthy that when anomaly detection is used with other methodologies, the overall detection effect is boosted.

Machine Learning Approaches

Machine learning methods are widely used in cybersecurity because they improve threat detection by analysing large data sets' patterns and deviations. The superiority of machine learning and deep learning strategies over the traditional signature-based methods while stressing how the new approach adapts better to emerging threats. These techniques are instrumental in identifying malware, phishing, and other threats through learning from the set data and increasing the accuracy of future detections (Halbouni et al., 2022). Applying the supervised and unsupervised learning algorithms makes it possible to analyze and act in real-time, making machine learning a pertinent component of the current cybersecurity solutions. This adaptability and perfection make machine learning a weapon in combating cyber Threats.

Anomaly Detection

In cybersecurity, anomaly detection reveals exceptions from the standard pattern and possibly security threats. Diverse approaches aimed at discovering new types of risk in hostile environments to which the system is exposed (Corallo et al., 2022). Such approaches entail using statistical models and machine learning approaches and integrating the two. Anomaly detection solutions can filter out and alert potentially malicious activities that rule-orientated systems may well miss when an application is determining general trends and outliers in data (Bierbrauer et al., 2021). Combined with other solutions, anomaly detection strengthens an organization’s security, giving the heads-up on potential threats. The processes of anomaly detection and developing methods for it are constant and evolving, and they contribute to the organization having a constant protective solution and not being caught by new cyber threats.

Risk Management

In cybersecurity, risk management is the best practice of recognizing, analyzing, and reducing risks that may affect an organization’s resources. Systems involve evaluating risk and the cost of investments to determine the most effective security measures to implement (Lee, 2021). Such frameworks assist management in planning and requesting resources correctly while having protective measures against cyber threats. These include daily scanning of the environment and risk changes so that necessary plans are developed to deal with possible risks. Effective risk management helps organizations reduce the effects of cyber threats and protect essential resources and processes. This results in the improved posturing of cyber security and assists in planning resources for the organization through security strategies for the future.

Frameworks and Models

Frameworks and models in cybersecurity are thus structured manageable structures to ensure improvement on the security structure of an organization. An evaluation of the various cybersecurity frameworks and information security standards will be conducted, and the topic of information security frameworks will be stressed as organisations’ references for proper security measures. Information security frameworks, including the NIST Cybersecurity Framework, define strategies for risk management and detection and response to cybersecurity threats (Taherdoost, 2022). One of the lessons that can be viewed and learnt by adopting standardized frameworks conforming to an organisation qualifications is the systematic way of developing clear-cut policies and procedures containing precluded risks associated with cybersecurity threats. These models are instrumental in ensuring compliance with industry regulations and standards and helping enhance cybersecurity capacity. Implementing robust frameworks enables organizations to enhance their resilience against cyber threats and maintain trust with stakeholders by demonstrating a commitment to cybersecurity best practices.

Risk Assessment Techniques

Methods used in risk assessment in cybersecurity are structured processes for evaluating the threats posed to an organization’s resources. Different approaches to risk assessment in smart city infrastructures and possibilities to use both qualitative and quantitative methods (Kalinin et al., 2021). These approaches help in risk assessment since they consider the weaknesses, the threats, and the probability of exploitation for efficiency in implementing security solutions. Utilizing multiple forms of assessments can provide a comprehensive assessment of the organization’s cybersecurity environment and make proper evaluations for rectification. Strict risk assessment measures reduce the levels of risk that an organization is exposed to and help the organization be better prepared to prevent attacks in the future.

User Behavior

User actions highly influence computer security risk results as users’ activities can expose systems to risks. Discuss the methods used by users in cybersecurity management and focus on the impact of people’s actions on security. Gaining knowledge about the users’ actions and intentions and adherence to security requirements is critical to developing proper security measures and education processes (Moustafa et al., 2021). Organizations can effectively manage human-related risks by raising user awareness and encouraging a security-oriented culture. Through behavioural analysis, vulnerabilities that may be linked to users’ activities become visible, allowing for preventive practices that make systems more secure against cyber threats. Teaching about user behaviour and providing appropriate behavioural interventions enhance total defence against cyber risks and threats. Top of FormBottom of Form

Human Factors in Cybersecurity

The users involve themselves in applying security measures in a given computer system, and human factors assume great importance in determining the levels of cybersecurity. This scoping review elaborated on the inconsistency in security practice, mainly in relation to cognitive biases, user perceptions, and organizational culture in the area of human factors in cybersecurity (Rahman et al., 2021). Awareness of these attributes is critical when implementing practical security measures that enforce security policies and avoid people’s mistakes. By resolving usability issues and improving user experiences, one can cultivate the security environment and take measures to minimize the risks. Integrating human aspects into cybersecurity processes guarantees that technical measures are compatible with human actions, thus boosting the organizational resistance to cyber threats.

Behavioral Interventions

Educationally related methodologies in cybersecurity are activities that target the change in the behavior of users in the right direction. This paper proposed the behavioral constructs that affect the user’s cybersecurity behaviour, focusing on the efficacy of the targeted interventions. Some of the measures are security awareness training, feedback and recognition programs and reward systems for compliance with security policies (Mashiane & Kritzinger, 2021). Entities deem it possible to improve the levels of compliance with set security policies and decrease human factors by influencing and addressing the behavioural factors comprising knowledge gaps and attitudes to security. Special measures aimed at separate categories of consumers contribute to creating an atmosphere of cybersecurity responsibility, which leads to creating a solid defence behind an organization’s walls from cyber threats.

Cybersecurity in Emerging Technologies

Emerging technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), present unique cybersecurity challenges due to their novelty and complexity. Lewallen (2021) explores the uncertainty in defining problems related to cybersecurity in these technologies, emphasizing that rapid development often outpaces the creation of robust security measures. This lag results in vulnerabilities that cyber threats can exploit. Lewallen suggests that addressing these issues requires a dynamic approach to cybersecurity regulation and governance that evolves alongside technological advancements to mitigate emerging risks effectively.

Conclusion

Based on the comprehensive review of current literature, this study proposes a hypothesis: The coordination of threat identification, sound risk management principles and behaviour modification considerably improve organisational security. The literature highlights the significance of the smart application of innovative and sophisticated solutions such as machine learning for threat identification, strict methodologies for risk management, and behavioural change initiatives targeting end-users. One of the general issues given to researchers is the lack of understanding of the combined influence of these components on overall cybersecurity efficiency. Future research should also be directed to understanding the relationship of these variables and their interaction effects. If these gaps are addressed, the organization could employ a more comprehensive strategy that would enhance the security status and deter other emerging threats, hence enhancing the protection of organizational digital resources and business continuance.

References

Bierbrauer, D. A., Chang, A., Kritzer, W., & Bastian, N. D. (2021). Cybersecurity anomaly detection in adversarial environments. arXiv preprint arXiv:2105.06742.

Corallo, A., Lazoi, M., Lezzi, M., & Luperto, A. (2022). Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review. Computers in Industry, 137, 103614.

Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine learning and deep learning approaches for cybersecurity: A review. IEEE Access, 10, 19572-19585.

Kalinin, M., Krundyshev, V., & Zegzhda, P. (2021). Cybersecurity risk assessment in smart city infrastructures. Machines, 9(4), 78.

Lee, I. (2021). Cybersecurity: Risk management framework and investment cost analysis. Business Horizons, 64(5), 659-671.

Mashiane, T., & Kritzinger, E. (2021). Identifying behavioral constructs in relation to user cybersecurity behavior. Eurasian Journal of Social Sciences, 9(2), 98-122.

Moustafa, A. A., Bello, A., & Maurushat, A. (2021). The role of user behaviour in improving cyber security management. Frontiers in Psychology, 12, 561011.

Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024). Machine learning in cybersecurity: A review of threat detection and defense mechanisms. World Journal of Advanced Research and Reviews, 21(1), 2286-2295.

Rahman, T., Rohan, R., Pal, D., & Kanthamanon, P. (2021, June). Human factors in cybersecurity: a scoping review. In Proceedings of the 12th International Conference on Advances in Information Technology (pp. 1-11).

Taherdoost, H. (2022). Understanding cybersecurity frameworks and information security standards—a review and comprehensive overview. Electronics, 11(14), 2181.

Lewallen, J. (2021). Emerging technologies and problem definition uncertainty: The case of cybersecurity. Regulation & Governance, 15(4), 1035-1052.