Abstract

In the ever-evolving field of cybersecurity, the ability to effectively prioritize threats is essential for organizations to allocate resources wisely and proactively mitigate potential risks. Avian Shield Optimizer (ASO) is a revolutionary optimization technique specifically designed for threat prioritization. ASO utilizes evolutionary-inspired mechanisms to dynamically adjust the prioritization of threats based on a comprehensive analysis of factors such as severity, relevance, and potential impact on organizational assets. By continuously monitoring and adapting to the dynamic threat landscape, ASO enables organizations to anticipate and respond quickly to emerging threats, ensuring that resources are allocated appropriately to address the most critical vulnerabilities. Moreover, ASO seamlessly integrates with existing threat intelligence platforms, providing cybersecurity teams with actionable insights and recommendations to comprehensively strengthen their defense strategies. Concurrently, the GraphForgeElite framework enhances this approach by facilitating the analysis of complex network data connections and structures, enabling the detection of subtle irregularities that may indicate potential cyber threats. ASO collaborates synergistically with GraphForgeElite, utilizing evolutionary-inspired methodologies to dynamically adapt the framework's design and settings, automatically tuning the parameters. Through rigorous experimentation, the performance of ASO-GraphForgeElite's Network is compared to other state-of-the-art classifiers. The results demonstrate the superior performance of ASO-GraphForgeElite's Network, surpassing 99% accuracy, precision, recall, and F1-score. Additionally, the framework exhibits efficiency in handling complex network data structures, enabling the identification of subtle patterns that indicate potential threats.

Details

Title
Enhancing Threat Prioritization In Cybersecurity Using Avian Shield Optimizer (ASO) In Graph Forge Elite (GFE) Framework
Author
Kumar, Vinoth R 1 ; Suguna, R 1 

 Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India 
Pages
2257-2271
Publication year
2024
Publication date
2024
Publisher
Engineering and Scientific Research Groups
e-ISSN
11125209
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3073677379
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.