A SYSTEM AND METHOD WITH ADAPTIVE AI-POWERED IOT SECURITY FOR REAL-TIME THREAT DETECTION IN SMART HOMES

Abstract

[020] The present invention relates to a System and Method with Adaptive AI-Powered IoT Security for Real-Time Threat Detection in Smart Homes. The Multi-Layered Adaptive AI-Powered IoT Security System (MAIS) provides a robust solution for smart home security by integrating adaptive, decentralized technologies. Using a multi-layered framework, the system combines context-aware threat detection, federated learning, behavioral profiling, edge computing, and blockchain-based audit trails to secure IoT networks. The system’s AI-driven layers detect and respond to threats in real time, dynamically adapting to emerging attack vectors without compromising user privacy. Its blockchain-enabled audit functionality ensures a transparent record of security events, making MAIS a reliable and efficient security solution for modern smart homes. Accompanied Drawing [FIG. 1]

Specification

Description:[019] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.

[020] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.

[021] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.

System Overview
[022] The Multi-Layered Adaptive AI-Powered IoT Security System (MAIS) leverages a layered approach to protect against cyber and physical threats in smart homes. Each layer of the system contributes to a comprehensive security solution, combining real-time threat analysis with adaptive learning capabilities. MAIS is designed to operate autonomously and maintain user privacy through decentralized threat detection and analysis mechanisms.

Components and Architecture
Context-Aware Threat Detection Layer
[023] Technical Details: This layer utilizes supervised and unsupervised machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze user and device behavior. The models are trained on context-specific data, including time, location, and user activity, allowing them to distinguish between normal activities and anomalies.
Example: If a smart lock is accessed during a typical user activity window, the system recognizes it as safe. However, if accessed at an unusual hour or from an unknown IP address, it triggers an alert.

Functionality: The layer continually adapts by updating its detection models using reinforcement learning, incorporating user feedback and activity data to improve detection accuracy over time.

Federated Learning-Enabled Decentralized Threat Analysis Layer
[024] Technical Details: This layer uses federated learning to enable multiple smart homes to contribute to a global threat model without compromising user privacy. Each smart home device trains a local model on threat data, which is then aggregated to improve the global model without sharing raw data.
Example: A malware signature detected by one smart camera updates the federated model, alerting other homes to similar patterns.
Functionality: By distributing model training and aggregation, this layer minimizes network latency and increases scalability, creating a community-driven defense mechanism that evolves with global threat patterns.

Behavioral Anomaly Detection Layer with Device Profiling
[025] Technical Details: This layer profiles each device based on observed behavioral patterns, using clustering algorithms or autoencoders for anomaly detection. Regular behavior is recorded, creating baselines that detect deviations such as abnormal data usage or unexpected communication with external networks.
Example: A smart thermostat suddenly communicating with an unfamiliar IP address prompts the system to isolate the device and alert the homeowner.
Functionality: This layer adapts continuously, retraining its profile baselines using reinforcement learning based on user feedback to refine its understanding of each device's normal operations.

Edge Computing Integration for Real-Time Threat Analysis
[026] Technical Details: Edge computing capabilities allow the system to process threat data locally on IoT devices or a centralized local hub, such as a smart home router. Lightweight machine learning models optimized for edge devices, such as TinyML, perform real-time inference and response tasks.
Example: If a security camera detects abnormal activity, it initiates immediate video recording and network isolation of suspicious devices without cloud dependency.
Functionality: This feature enhances the system's responsiveness by reducing data transmission delays, which is critical in responding to real-time security threats.

Dynamic Threat Response Layer
[027] Technical Details: This layer deploys rule-based and AI-generated protocols for responding to detected threats. The system categorizes threats into severity levels, triggering responses ranging from notification alerts to device disconnection.
Example: A high-severity threat, such as a detected intrusion, initiates a lockdown protocol, isolating devices and notifying homeowners and security services.

Functionality: The response layer includes customizable protocols, allowing users to specify preferred actions for different types of threats, or rely on AI-driven suggestions based on historical responses and outcomes.

Blockchain-Enabled Audit Trail
Technical Details: A blockchain ledger records all security events, including threat detections, response actions, and system updates. Each event is logged with a timestamp and cryptographic signature, ensuring data integrity.
Example: If a user wants to review device activity after a detected intrusion, they can access an immutable record through the blockchain ledger.

Functionality: This ledger serves as a reliable, tamper-proof record of all system activities, supporting accountability and providing audit capabilities for users and security professionals.
[028] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.

[029] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.

[030] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention. , Claims:1.A multi-layered security system for IoT-enabled smart homes, comprising a context-aware threat detection layer, federated learning-enabled threat analysis, behavioral anomaly detection, edge computing integration, and a dynamic response layer, characterized by its adaptive, decentralized approach to threat detection and mitigation.

2.The system of Claim 1, wherein the context-aware threat detection layer uses AI algorithms to differentiate between normal and suspicious activities based on user and device behavioral analysis.

3.The system of Claim 1, wherein federated learning is used to aggregate threat data across multiple IoT-enabled smart homes, enhancing threat detection capabilities without sharing individual user data.

4.The system of Claim 1, wherein the edge computing integration enables local data processing and real-time threat analysis on IoT devices, improving response times and minimizing the need for cloud dependency.

5.The system of Claim 1, further comprising a blockchain-based audit trail that maintains an immutable record of security events, allowing for transparency and data integrity verification.

6.The system of Claim 1, wherein the dynamic response layer can execute automated security protocols, including device isolation, alerting, and network segmentation based on threat severity.

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