Algorithm innovation and integration with big data technology are essential aspects of advancing information security. This study presents the current status and future development of these areas. Firstly, the existing algorithms in information security are reviewed, highlighting their functionalities and importance. However, challenges such as computational complexity and key management hinder their effectiveness. Secondly, the application of big data technology in information security is discussed, focusing on its role in log analysis, threat intelligence, and behavioral analytics. Despite its benefits, integrating big data technology poses challenges related to data privacy and resource constraints. Thirdly, the intersection of algorithm innovation and big data technology is explored, emphasizing the opportunities for algorithm development and the advantages of leveraging big data for this purpose. By harnessing big data analytics, algorithm developers can enhance the performance and scalability of security algorithms, leading to more effective threat detection and incident response.

Chensha Wang, Yu Li, Lijing Liu. Algorithm Innovation and Integration with Big Data Technology in the Field of Information Security: Current Status and Future Development. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 2: 45-49. https://doi.org/10.25236/AJETS.2024.070208.

[1] Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, 101827.

[2] Ahmadi, S. (2024). A Comprehensive Study on Integration of Big Data and AI in Financial Industry and its Effect on Present and Future Opportunities. International Journal of Current Science Research and Review, 7(01), 66-74.

[3] Yang, C., Clarke, K., Shekhar, S., & Tao, C. V. (2020). Big Spatiotemporal Data Analytics: A research and innovation frontier. International Journal of Geographical Information Science, 34(6), 1075-1088.

[4] Sreedevi, A. G., Harshitha, T. N., Sugumaran, V., & Shankar, P. (2022). Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review. Information Processing & Management, 59(2), 102888.

[5] Xu, L., Jiang, C., Wang, J., Yuan, J., & Ren, Y. (2014). Information security in big data: privacy and data mining. Ieee Access, 2, 1149-1176.

[6] Bansal, M., Chana, I., & Clarke, S. (2020). A survey on iot big data: current status, 13 v’s challenges, and future directions. ACM Computing Surveys (CSUR), 53(6), 1-59.

[7] Bresciani, S., Ciampi, F., Meli, F., & Ferraris, A. (2021). Using big data for co-innovation processes: Mapping the field of data-driven innovation, proposing theoretical developments and providing a research agenda. International Journal of Information Management, 60, 102347.