Computer Vision-Based Anomaly Detection in DevOps
Machine Learning for Automated Infrastructure Security
Keywords:
DevOps, anomaly detection, computer vision, threat detection, risk mitigationAbstract
In an increasingly digital world, the security of infrastructure is paramount, especially in DevOps environments that emphasize rapid development and deployment. This paper investigates the integration of computer vision and machine learning for anomaly detection within DevOps, focusing on automating security monitoring to ensure timely responses to infrastructure anomalies. Traditional methods of anomaly detection often rely on heuristic or rule-based systems that may fail to identify novel threats, resulting in vulnerabilities that can be exploited by malicious actors. By leveraging computer vision techniques, organizations can monitor physical and virtual environments in real time, analyzing visual data to identify unusual patterns or behaviors indicative of potential security breaches. This research discusses the methodologies employed in developing such systems, explores their impact on DevOps practices, and addresses the challenges associated with their implementation. The findings suggest that combining machine learning with computer vision can significantly enhance security measures in DevOps environments, facilitating proactive incident response and risk mitigation.
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References
Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.
George, Jabin Geevarghese, and Arun Rasika Karunakaran. "Enabling Scalable Financial Automation in Omni-Channel Retail: Strategies for ERP and Cloud Integration." Human-Computer Interaction Perspectives 1.2 (2021): 10-49.
Yellepeddi, Sai Manoj, et al. "AI-Powered Intrusion Detection Systems: Real-World Performance Analysis." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 279-289.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.
Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.
Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.
Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.
Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.
Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.
Alluri, Venkat Rama Raju, et al. "Automated Testing Strategies for Microservices: A DevOps Approach." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 101-121.
S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.
C. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1097–1105.
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