Threat Modeling Techniques - Risk Assessment Methods: Studying threat modeling techniques and risk assessment methods for identifying and prioritizing potential cyber threats and vulnerabilities
Keywords:
Threat Modeling, Risk AssessmentAbstract
Threat modeling is a critical process in cybersecurity that involves identifying, assessing, and prioritizing potential threats and vulnerabilities to a system. This paper provides an overview of various threat modeling techniques and risk assessment methods used in cybersecurity. The goal is to help organizations understand the importance of threat modeling and how it can be effectively implemented to enhance their security posture. The paper discusses the key concepts of threat modeling, including asset identification, threat identification, vulnerability assessment, risk analysis, and risk mitigation strategies. It also explores the different approaches to threat modeling, such as STRIDE, DREAD, and PASTA, and how they can be applied in practice. Additionally, the paper examines the challenges and limitations of threat modeling and provides recommendations for improving its effectiveness. Overall, this paper aims to serve as a comprehensive guide for cybersecurity professionals and organizations looking to enhance their threat modeling capabilities.
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References
K. Joel Prabhod, “ASSESSING THE ROLE OF MACHINE LEARNING AND COMPUTER VISION IN IMAGE PROCESSING,” International Journal of Innovative Research in Technology, vol. 8, no. 3, pp. 195–199, Aug. 2021, [Online]. Available: https://ijirt.org/Article?manuscript=152346
Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
Tatineni, Sumanth, and Anjali Rodwal. “Leveraging AI for Seamless Integration of DevOps and MLOps: Techniques for Automated Testing, Continuous Delivery, and Model Governance”. Journal of Machine Learning in Pharmaceutical Research, vol. 2, no. 2, Sept. 2022, pp. 9-41, https://pharmapub.org/index.php/jmlpr/article/view/17.
Makka, Arpan Khoresh Amit. “Integrating SAP Basis and Security: Enhancing Data Privacy and Communications Network Security”. Asian Journal of Multidisciplinary Research & Review, vol. 1, no. 2, Nov. 2020, pp. 131-69, https://ajmrr.org/journal/article/view/187.
Pulimamidi, Rahul. "Leveraging IoT Devices for Improved Healthcare Accessibility in Remote Areas: An Exploration of Emerging Trends." Internet of Things and Edge Computing Journal 2.1 (2022): 20-30.
Gudala, Leeladhar, et al. "Leveraging Biometric Authentication and Blockchain Technology for Enhanced Security in Identity and Access Management Systems." Journal of Artificial Intelligence Research 2.2 (2022): 21-50.
Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "Exploiting the Power of Machine Learning for Proactive Anomaly Detection and Threat Mitigation in the Burgeoning Landscape of Internet of Things (IoT) Networks." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 30-58.
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
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