Resilient IOT Ecosystems Through Predictive Maintenance and AI Security Layers

EasyChair Preprint 15693

Abstract

The increasing use of IoT devices in different industries means that the resulting ecosystems must be
reliable and capable of quickly recovering from cyber threats. This study addresses IoT reliability and incorporates
communication with predictive maintenance and AI security layers. The framework uses predictive analytical tools,
which means the framework predicts device breakdowns and security threats so that measures can be taken in advance.
At the same time, AI in security uses a range of machine learning algorithms for progressive threat tracking and
adaptive patching to offer perpetual security against innovative threats.
Determined results present insights into how AI improves vulnerability detection, minimizing exposure to attacks.
Further, accurate dynamic patch management carried out by AI does not cause frequent operational interruptions; it
also manages the integration of security patching without input from an individual. It enhances IoT safety and improves
device efficiency via preventive maintenance approaches.
This integrated approach presents many advantages in various fields, including healthcare, manufacturing, energy, and
smart cities. Better protection of IoT systems guarantees business and administrative availability and protection of
crucial infrastructures and data. Moreover, the framework is an enabler of future IoT security reference architectures
and structures. It provides the basis for the self-protective and self-aware defense mechanisms necessary for sustainable
new IoT systems.

Keyphrases: AI-driven security, Cybersecurity, Dynamic Patch Management, IoT Ecosystems, IoT Resilience, Predictive Maintenance, vulnerability detection

@booklet{EasyChair:15693,
  author    = {Sina Ahmadi and Chi Wan},
  title     = {Resilient IOT Ecosystems Through Predictive Maintenance and AI Security Layers},
  howpublished = {EasyChair Preprint 15693},
  year      = {EasyChair, 2025}}