Future Trends in AI and Machine Learning for Cybersecurity
Main Article Content
Abstract
The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has transformed the landscape of cybersecurity, enabling organizations to combat increasingly sophisticated cyber threats. This research paper explores the future trends in AI and ML within the cybersecurity domain, focusing on emerging technologies, methodologies, and applications that are set to reshape the industry. We examine the potential of AI-driven predictive analytics, automated threat detection and response, and adaptive learning systems that continuously evolve in response to new threats. Additionally, the integration of natural language processing (NLP) for analyzing and interpreting unstructured data, such as social media feeds and security logs, is discussed. The paper highlights the challenges that accompany these advancements, including data privacy concerns, algorithmic bias, and the need for transparency and accountability in AI systems. By analyzing current case studies and forecasting future developments, we provide insights into how organizations can leverage AI and ML to enhance their cybersecurity strategies. Ultimately, this research underscores the importance of a proactive approach to adopting AI and ML technologies, emphasizing collaboration between human expertise and automated systems to create resilient cybersecurity infrastructures in an evolving threat landscape.
Downloads
Article Details
How to Cite
References
Kim, G., Humble, J., & Debois, P. (2016). The DevOps Handbook: How to Create World-Class Agility, Reliability, & Security in Technology Organizations. IT Revolution Press.
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
Rubinoff, S. (2018). Web and Network Data Science: Modeling Techniques in Predictive Analytics. CRC Press.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Kinetics of a single cross-bridge in familial hypertrophic cardiomyopathy heart muscle measured by reverse Kretschmann fluorescence. Journal of Biomedical Optics, 15(1), 017011-017011.
Mettikolla, P., Luchowski, R., Gryczynski, I., Gryczynski, Z., Szczesna-Cordary, D., & Borejdo, J. (2009). Fluorescence lifetime of actin in the familial hypertrophic cardiomyopathy transgenic heart. Biochemistry, 48(6), 1264-1271.
Mettikolla, P., Calander, N., Luchowski, R., Gryczynski, I., Gryczynski, Z., & Borejdo, J. (2010). Observing cycling of a few cross‐bridges during isometric contraction of skeletal muscle. Cytoskeleton, 67(6), 400-411.
Muthu, P., Mettikolla, P., Calander, N., & Luchowski, R. 458 Gryczynski Z, Szczesna-Cordary D, and Borejdo J. Single molecule kinetics in, 459, 989-998.
Chandrasekaran, K. C., & Meghanathan, N. (2017). Big Data Analytics: A Hands-On Approach. CRC Press.
Ransford, B., Clarke, D., & Duquennoy, S. (2019). Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Access, 7, 12950-12988.
Forsgren, N., Humble, J., & Kim, G. (2018). Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations. IT Revolution Press.
Parnin, C., & Bird, C. (2016). Usage, costs, and benefits of continuous integration in open-source projects. Empirical Software Engineering, 21(3), 1-35.
Pombinho, J., & Silva, A. R. (2018). DevSecOps: Shifting Security Left with Continuous Delivery. Proceedings of the 1st International Workshop on Secure Development Lifecycle.
Pires, M., & Duboc, L. (2017). Towards a DevSecOps process model: Organizational patterns of integration of security in DevOps. Journal of Systems and Software, 130, 141-159.
Rubinoff, S., & Rajkumar, T. (2016). Applied Data Science: Lessons Learned for the Data-Driven Business. O'Reilly Media.
Ransford, B., Clarke, D., & Duquennoy, S. (2019). Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Access, 7, 12950-12988.
Chandrasekaran, K. C., & Meghanathan, N. (2017). Big Data Analytics: A Hands-On Approach. CRC Press.
Rubinoff, S. (2018). Web and Network Data Science: Modeling Techniques in Predictive Analytics. CRC Press.
Liu, S., Yu, S., & Guo, Y. (2019). A survey on security threats and defensive techniques of machine learning: A data driven view. Journal of Network and Computer Applications, 131, 36-57.
Parnin, C., & Bird, C. (2016). Usage, costs, and benefits of continuous integration in open-source projects. Empirical Software Engineering, 21(3), 1-35.
Bass, L., Weber, I., & Zhu, L. (2015). DevOps: A Software Architect’s Perspective. Addison-Wesley.
Haines, M., & Righter, R. (2016). Securing DevOps: Security in the Cloud. O'Reilly Media.
Fitzgerald, B., Stol, K. J., & O'Sullivan, P. (2014). Continuous software engineering and beyond: Trends and challenges. Information and Software Technology, 56(5), 365-386.
Le, V. H., & Chua, T. S. (2017). A survey on data fusion in the era of big data. ACM Computing Surveys (CSUR), 49(1), 1-42.
O'Reilly, T., & Battelle, J. (2009). Web Squared: Web 2.0 Five Years On. O'Reilly Media.
Luiijf, E. A., & Buijs, J. C. (2017). Securing Smart Cities. Springer.
Yadav, H. (2023). Securing and Enhancing Efficiency in IoT for Healthcare Through Sensor Networks and Data Management. International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-9.
Yadav, H. (2023). Enhanced Security, Privacy, and Data Integrity in IoT Through Blockchain Integration. International Journal of Sustainable Development in Computing Science, 5(4), 1-10.
Yadav, H. (2023). Advancements in LoRaWAN Technology: Scalability and Energy Efficiency for IoT Applications. International Numeric Journal of Machine Learning and Robots, 7(7), 1-9.