Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study
Abstract
The increasing sophistication of cyber threats has prompted organizations to seek more effective cybersecurity solutions. This research paper presents a comparative study evaluating AI-enhanced cybersecurity solutions against traditional cybersecurity methods. We examine various AI-driven approaches, including machine learning algorithms, natural language processing, and automated threat detection systems, alongside conventional techniques such as signature-based detection and heuristic analysis. The paper assesses the effectiveness of these methods in terms of detection rates, response times, adaptability to evolving threats, and overall cost-effectiveness. Through a comprehensive analysis of case studies and empirical data, we identify the strengths and weaknesses of each approach, highlighting the scenarios in which AI solutions outperform traditional methods and vice versa. Furthermore, we address challenges associated with AI implementation, including data quality, interpretability, and the potential for adversarial attacks. The findings underscore the transformative potential of AI in enhancing cybersecurity resilience, while also acknowledging the importance of integrating traditional methods into a holistic security framework. Ultimately, this study aims to provide insights for cybersecurity professionals and organizations seeking to optimize their security strategies in an increasingly complex digital landscape
References
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176-189.
Fehling, C., Leymann, F., Retter, R., Schupeck, W., & Arbitter, P. (2013). Cloud computing patterns: Fundamentals to design, build, and manage cloud applications. Springer.
Kopp, D., Hanisch, M., Konrad, R., & Satzger, G. (2020). Analysis of AWS Well-Architected Framework Reviews. In International Conference on Business Process Management (pp. 317-332). Springer.
Aghera, S. (2021). SECURING CI/CD PIPELINES USING AUTOMATED ENDPOINT SECURITY HARDENING. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 18(1).
Zhang, Q., Cheng, L., & Boutaba, R. (2011). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 2(1), 7-18.
Forsgren, N., Humble, J., & Kim, G. (2019). Accelerate: The science of lean software and DevOps: Building and scaling high performing technology organizations. IT Revolution Press.
Dhiman, V. (2021). ARCHITECTURAL DECISION-MAKING USING REINFORCEMENT LEARNING IN LARGE-SCALE SOFTWARE SYSTEMS. International Journal of Innovation Studies, 5(1).
Dhiman, V. (2020). PROACTIVE SECURITY COMPLIANCE: LEVERAGING PREDICTIVE ANALYTICS IN WEB APPLICATIONS. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 17(1).
Dhiman, V. (2019). DYNAMIC ANALYSIS TECHNIQUES FOR WEB APPLICATION VULNERABILITY DETECTION. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 16(1).
Besker, T., Bastani, F., & Trompper, A. (2018). A Model-Driven Approach for Infrastructure as Code. In European Conference on Service-Oriented and Cloud Computing (pp. 72-87). Springer.
Armbrust, M., & Zaharia, M. (2010). Above the Clouds: A Berkeley View of Cloud Computing. EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28.
Muthu, P., Mettikolla, P., Calander, N., & Luchowski, R. 458 Gryczynski Z, Szczesna-Cordary D, and Borejdo J. Single molecule kinetics in, 459, 989-998.
Borejdo, J., Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., & Gryczynski, Z. (2021). Surface plasmon assisted microscopy: Reverse kretschmann fluorescence analysis of kinetics of hypertrophic cardiomyopathy heart.
Mettikolla, Y. V. P. (2010). Single molecule kinetics in familial hypertrophic cardiomyopathy transgenic heart. University of North Texas Health Science Center at Fort Worth.
Mettikolla, P., Luchowski, R., Chen, S., Gryczynski, Z., Gryczynski, I., Szczesna-Cordary, D., & Borejdo, J. (2010). Single Molecule Kinetics in the Familial Hypertrophic Cardiomyopathy RLC-R58Q Mutant Mouse Heart. Biophysical Journal, 98(3), 715a.
Kavis, M. J. (2014). Architecting the Cloud: Design Decisions for Cloud Computing Service Models (SaaS, PaaS, and IaaS). John Wiley & Sons.
Zhang, J., Cheng, L., & Boutaba, R. (2010). Cloud computing: a survey. In Proceedings of the 2009 International Conference on Advanced Information Networking and Applications (pp. 27-33).
Jones, B., Gens, F., & Kusnetzky, D. (2009). Defining and Measuring Cloud Computing: An Executive Summary. IDC White Paper.
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