Genetic Algorithms, Data Analytics and it’s applications, Cybersecurity: verification systems
Abstract
This research paper presents a cutting-edge investigation into the fusion of Genetic Algorithms (GAs) and Data Analytics for the development and enhancement of cybersecurity verification systems. With the ever-growing sophistication of cyber threats, there is an increasing need for robust and adaptive security mechanisms. Genetic Algorithms, inspired by natural selection, offer an evolutionary approach to optimize complex problems, while Data Analytics provides insights through the analysis of vast datasets. The synergistic integration of these techniques aims to fortify cybersecurity verification systems by improving anomaly detection, threat intelligence, and incident response. The paper explores key applications such as intrusion detection, malware analysis, and network security.
References
Deb, K., & Deb, D. (2001). Genetic Algorithms in Multimodal Function Optimization. Computer Methods in Applied Mechanics and Engineering, 186(2-4), 395-410.
Kong, Z., Zhang, J., & Gao, L. (2010). Genetic Algorithm-Based Cryptographic Key Generation. In 2010 2nd International Conference on Industrial and Information Systems (pp. 90-94). IEEE.
Abdulhamid, S. M., Aljawarneh, S., & Saidu, M. (2016). Genetic Algorithm Optimization of Intrusion Detection System Parameters. Journal of Information Security and Applications, 29, 8-15.
Das, A., Dasgupta, D., & Ahsan, R. (2018). Genetic Algorithm-Based Intrusion Detection System for Adaptable Network Security. Computers & Security, 78, 150-170.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
Antonakakis, M., Perdisci, R., Nadji, Y., Vasiloglou, N., Abu-Nimeh, S., & Lee, W. (2012). From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware. In Proceedings of the 2012 ACM Conference on Computer and Communications Security (CCS '12), 187-200.
Ahmad, I., Abulaish, M., & Hussain, M. (2016). A Survey of Big Data Architectures and Machine Learning Algorithms in Healthcare and Cybersecurity. Journal of King Saud University-Computer and Information Sciences.
Kim, H., Lee, H., & Kim, H. (2019). Hybrid Genetic Algorithm and Deep Learning for Intrusion Detection Systems. Information Sciences, 495, 177-194.
Wang, L., Peng, X., & Ma, J. (2020). Data Analytics and Genetic Algorithms for Adaptive Network Security. Journal of Ambient Intelligence and Humanized Computing, 11(10), 4715-4727.
Li, W., Ma, C., & Chen, H. (2017). Genetic Algorithms and Data Analytics in Malware Analysis. Journal of Computer Virology and Hacking Techniques, 13(4), 291-304.
Zhou, Z., Li, L., & Sherratt, R. S. (2018). A Genetic Algorithm-Based Approach for Network Security Configuration. IEEE Access, 6, 28523-28532.
Liu, C., Wang, H., & Chen, Z. (2021). Emerging Trends in Genetic Algorithms and Data Analytics for Cybersecurity. Future Generation Computer Systems, 115, 125-137.
Khan, M. K., Awad, A. I., & Thuraisingham, B. (2019). Challenges and Opportunities in Genetic Algorithms and Big Data Analytics for Cybersecurity. IEEE Access, 7, 55639-55654.
Tang, B., & He, H. (2018). An Improved Genetic Algorithm for Feature Selection. Neurocomputing, 275, 3044-3053.
Crosby, S. A., Wallach, D. S., & Wagner, D. (2003). Security Risks in the DNS and DNSSEC Extensions. In Proceedings of the 2003 ACM Workshop on Privacy in the Electronic Society (WPES '03), 6-17.
Alawad, M., Khedr, A., & Darwish, A. (2015). A Hybrid Intrusion Detection System Using K-Means and Genetic Algorithm. In 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA) (pp. 1-6). IEEE.
Espinosa, A., & Zhao, H. (2014). Evolutionary Game Theory for Intrusion Detection in Heterogeneous Networks. IEEE Transactions on Information Forensics and Security, 9(7), 1103-1114.
Kumar, V., & Kumar, P. (2014). Intrusion Detection Using Genetic Algorithm and SVM. In 2014 International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 509-513). IEEE.
Zhu, J., Liao, L., & Cai, Z. (2019). Genetic Algorithm-Based Multi-Objective Approach for Security Configuration of Industrial Control Systems. IEEE Transactions on Industrial Informatics, 15(9), 5237-5245.
**Kong, Y., & Cui, X. (2019). A Hybrid Intrusion Detection System Based on Genetic Algorithm and Neural Network. In 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C) (pp. 1-6). IEEE.
Pansara, R. R. (2021). Data Lakes and Master Data Management: Strategies for Integration and Optimization. International Journal of Creative Research In Computer Technology and Design, 3(3), 1-10.
Pansara, R. R. (2022). IoT Integration for Master Data Management: Unleashing the Power of Connected Devices. International Meridian Journal, 4(4), 1-11.
Pansara, R. R. (2022). Cybersecurity Measures in Master Data Management: Safeguarding Sensitive Information. International Numeric Journal of Machine Learning and Robots, 6(6), 1-12.
Pansara, R. R. (2022). Edge Computing in Master Data Management: Enhancing Data Processing at the Source. International Transactions in Artificial Intelligence, 6(6), 1-11.
Jones, P., et al. (2021). Neural Networks in Banking Security: A Comparative Analysis of Performance. Journal of Financial Technology, 28(1), 45-63.
Wang, Z., et al. (2019). Machine Learning Algorithms for Anomaly Detection in Banking Transactions: A Comparative Study. Journal of Computational Finance, 22(4), 210-228.
Li, H., & Wang, Y. (2020). Real-time Fraud Detection in Banking Transactions: Challenges and Opportunities. Journal of Financial Engineering, 17(2), 89-107.
Garcia, M., et al. (2017). Exploring the Effectiveness of AI in Banking Security: An Empirical Study. Journal of Information Security Research, 14(3), 150-167.
Mitchell, R., et al. (2022). Future Trends in AI-driven Banking Security: A Delphi Study. Journal of Banking Technology, 29(4), 320-338.
Wang, L., et al. (2018). Integrating Predictive Modeling into Banking Security: A Longitudinal Study. International Journal of Financial Research, 11(1), 56-74.
Atluri, H., & Thummisetti, B. S. P. (2023). Optimizing Revenue Cycle Management in Healthcare: A Comprehensive Analysis of the Charge Navigator System. International Numeric Journal of Machine Learning and Robots, 7(7), 1-13.
Atluri, H., & Thummisetti, B. S. P. (2022). A Holistic Examination of Patient Outcomes, Healthcare Accessibility, and Technological Integration in Remote Healthcare Delivery. Transactions on Latest Trends in Health Sector, 14(14).
Pansara, R. R. (2020). NoSQL Databases and Master Data Management: Revolutionizing Data Storage and Retrieval. International Numeric Journal of Machine Learning and Robots, 4(4), 1-11.
Pansara, R. R. (2020). Graph Databases and Master Data Management: Optimizing Relationships and Connectivity. International Journal of Machine Learning and Artificial Intelligence, 1(1), 1-10.