Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection

Siva Subrahmanyam Balantrapu

Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection

Authors

Siva Subrahmanyam Balantrapu

Abstract

The rise of cyber threats has necessitated the development of advanced detection techniques to safeguard sensitive information and infrastructure. This research paper explores current trends and future directions in leveraging machine learning (ML) techniques for cyber threat detection. We examine the efficacy of various ML algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, in identifying and mitigating cyber threats across different domains, including network security, endpoint protection, and application security. The paper provides a comprehensive overview of recent advancements in feature extraction, anomaly detection, and classification methods, emphasizing their practical applications in real-world scenarios. Additionally, we analyze the challenges associated with implementing ML in cybersecurity, including data quality, model interpretability, and the risk of adversarial attacks. By reviewing existing literature and case studies, we highlight emerging trends such as the integration of deep learning and AI-driven automation in threat detection systems. The findings underscore the importance of ongoing research and innovation in machine learning to enhance cyber threat detection capabilities.

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.

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.

Yadav, H. (2024). Scalable ETL pipelines for aggregating and manipulating IoT data for customer analytics and machine learning. International Journal of Creative Research In Computer Technology and Design, 6(6), 1-30.

Yadav, H. (2024). Anomaly detection using Machine Learning for temperature/humidity/leak detection IoT. International Transactions in Artificial Intelligence, 8(8), 1-18.

Yadav, H. (2024). Structuring SQL/NoSQL databases for IoT data. International Journal of Machine Learning and Artificial Intelligence, 5(5), 1-12.

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.

Whig, P., Remala, R., Mudunuru, K. R., & Quraishi, S. J. (2024). Integrating AI and Quantum Technologies for Sustainable Supply Chain Management. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 267-283). IGI Global.

Whig, P., Mudunuru, K. R., & Remala, R. (2024). Quantum-Inspired Data-Driven Decision Making for Supply Chain Logistics. In Quantum Computing and Supply Chain Management: A New Era of Optimization (pp. 85-98). IGI Global.

Mudunuru, K. R., Remala, R., & Nagarajan, S. K. S. (2024). AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond.

Remala, R., Mudunuru, K. R., Gami, S. J., & Nagarajan, S. K. S. (2024). Optimizing Data Management Strategies: Analyzing Snowflake and DynamoDB for SQL and NoSQL. Journal Homepage: http://www. ijmra. us, 14(8).

Remala, R., Marupaka, D., & Mudunuru, K. R. (2024). Beyond Volume: Enhancing Data Quality in Big Data Analytics through Frameworks and Metrics.

Nagarajan, S. K. S., Remala, R., Mudunuru, K. R., & Gami, S. J. Automated Validation Framework in Machine Learning Operations for Consistent Data Processing.

Mudunuru, K. R., Remala, R., & Nagarajan, S. K. S. Leveraging IoT and Data Analytics in Logistics: Optimized Routing, Safety, and Resource Planning.

Remala, R., Mudunuru, K. R., & Nagarajan, S. K. S. Optimizing Data Ingestion Processes using a Serverless Framework on Amazon Web Services.

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.

Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection