International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
www.irjet.net
p-ISSN: 2395-0072
DATA PRIVACY AND SECURITY CONSIDERATIONS IN SELF-HEALING NETWORKS: BALANCING AUTOMATION AND CONFIDENTIALITY Sree Harsha Aruba HPE, USA -----------------------------------------------------------------------***-----------------------------------------------------------------------ABSTRACT Network management and maintenance are being revolutionized by self-healing networks, which utilize machine learning (ML) and artificial intelligence (AI) techniques. The advanced systems detect, diagnose, and resolve network issues in realtime, ensuring optimal performance and minimizing downtime [1]. Through the utilization of ML algorithms, extensive network data can be analyzed to detect patterns, enabling self-healing networks to anticipate and address potential faults and anomalies in advance [2]. AI techniques, like neural networks and deep learning, allow these systems to learn from past network behavior and adjust to changing conditions [3]. Self-healing networks utilize AI-based decision-making algorithms to identify the most efficient remediation strategies, taking into account objectives such as reducing downtime and optimizing throughput [4]. In addition, these networks demonstrate the ability to learn on their own, constantly improving their algorithms through feedback from previous actions. This results in a higher level of expertise in resolving issues autonomously as time goes on [5]. Network infrastructures are becoming more complex and dynamic. Self-healing networks can enhance resilience, reduce the need for manual intervention, and ensure seamless connectivity in domains such as telecommunications, data centers, and the Internet of Things (IoT) [6]. Keywords: Self-healing networks, Machine learning in network management, Artificial intelligence in network maintenance, Network resilience and optimization, Autonomous fault detection and remediation
INTRODUCTION The exponential growth of network complexity and the increasing demand for reliable, high-performance connectivity have presented significant challenges for conventional network management approaches [7]. Detecting, diagnosing, and resolving network issues has become more time-consuming and inefficient, resulting in extended periods of downtime and subpar network performance [8]. In order to tackle these challenges, the concept of self-healing networks has emerged as a transformative solution. It harnesses the power of machine learning (ML) and artificial intelligence (AI) techniques to enable autonomous network management [1]. Self-healing networks strive to automatically detect, diagnose, and resolve
© 2024, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 2018