An Efficient AI and Deep learning Assisted Self-Healing Network Approach: Analysis on Fault Detection Response and Recovery to Mitigate Threats in IoT-Security Ecosystem

Abstract

Artificial intelligence, machine learning (ML) is transforming the self-healing systems. These are mechanisms that are able to detect issues, anticipate on their occurrence and repair themselves without the assistance of an individual. In this paper, the author discusses how AI and ML may be applied to state-of-the-art self-healing systems with the help of predictive analysis, anomaly discovery, and automated recovery of the network. We discuss artificial intelligence methods such as neural networks, decision trees and reinforcement learning that are employed to predict and model failures in systems. In learning with historical and real-time data, it is possible to make predictions of a problem and begin correcting it before such a problem occurs, utilizing modern networks using ML algorithms. The article also compares the existing processes and outlines a framework for how predictive analytics and recovery algorithms can work together in real-time. As evidence of successful and secure AI-driven self-healing computer systems, we consider various practical applications and case studies in such fields as self-driving automobiles, cloud computing, and industrial automation. A few of the issues that arise include model accuracy, data quality and moral issues. Tables and figures present the performance of the proposed architecture and various models of ML. The integration of machine learning and self-healing capabilities indicates a significant step towards the creation of adaptable AI systems. The article contributes to the novel artificial- intelligence and machine-learning framework of reliable Internet-of-Things (IoT) networks, autonomic computing via a MAPE-K loop, reinforcement learning and bio-inspired concepts to achieve proactive defense mechanisms with over 90% detection rates and a recovery time of less than 5 minutes. The paper is characterized by the quality of literature assessment, clear statement of research issue, problematics and the presentation of hybrid solutions addressing some of the most common IoT threats, including distributed denial-of-service (DDoS) and zero-day exploits. However, the paper is significantly reliant on the simulation-based assessment to a significant degree and only slight empirical verification of hardware and scaling issues in the heterogeneous deployments can be observed. The current review includes quantitative studies, the most critical tableaux, derivational mathematics and constructive suggestions such as improved empirical testing and interdisciplinary harmonization that will help improve the viability of the system in the real world. Summing up, this work has an important contribution to the area of IoT security but it requires additional improvement to gain a more practical applicability and influence. The paper is concluded with the future opportunities and the possibility of the popularization of AI usage in the process of developing self-healing ecosystems.