Explainable AI for Secure and Trustworthy Autonomous Network Management
subject: Autonomous Network Management; Cybersecurity; Explainable AI; Interpretability; Trustworthy AI
description: Rise of AI-driven autonomous networks for managing complex, dynamic infrastructures. While AI optimizes performance, it acts as a black box. This lack of transparency undermines trust and security, making it challenging to validate decisions, detect adversarial attacks, and understand why an AI model made a specific routing, security, or resource allocation decision. Security blind spots face significant challenges in detecting subtle adversarial manipulations or policy exploits because the reasoning behind the model's decisions is hidden. Additionally, poor diagnosability occurs when a network fault or performance degradation occurs, making root cause analysis slow and complex. Hence, the network operators are hesitant to cede control to systems whose actions they cannot verify or audit. Explainable AI (XAI) is critical for bridging this gap, ensuring management decisions are transparent, interpretable, and defensible. The proposed model makes real-Time management decisions. This model uses post-hoc techniques to generate explanations for each decision. It presents actionable insights and cross-references explanations against security policies and known threat patterns to flag anomalous reasoning. 2025 IEEE.
creator: Kandi, Yash; Srivastava, Durgesh; Logeshwaran, J.; Dhanasekaran, S.; Sama, Mukhtar; Garg, Amit
source: Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.902-906
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155787-4;
language: English
type: Conference paper
coverage: Kandi Y., Manipal University, Jaipur, Department of Computer and Communication Engineering, Rajasthan, Jaipur, India; Srivastava D., Chitkara University, Punjab and Faculty of Computing and IT, Sohar University, DCSE, Oman; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Dhanasekaran S., Sri Eshwar College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, Coimbatore, India; Sama M., Marwadi University, Department of Mechanical Engineering, Gujarat, Rajkot, India; Garg A., Manipal University Jaipur, Department of Computer Science and Engineering, Rajasthan, Jaipur, India