IoT Management System for Anomalous Device Remediation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current IoT management platforms struggle to effectively identify and remediate anomalous behaviors in large-scale IoT networks due to their inability to handle the scale, heterogeneity, and rapid security breaches, which overwhelms network administration.

Innovation Solution

An IoT management system that identifies and presents anomalous devices through a user-friendly interface, allowing network administrators to quickly remediate issues by providing real-time critical information and automating investigation and diagnosis, utilizing machine learning models and secure connections across multiple layers of the network.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If current IoT management platforms are used to monitor and manage large-scale IoT networks, then device management coverage is maintained, but network administration becomes overwhelmed due to the scale and rapidity of security breaches

Engineering Contradiction:

Improvenumber of IoT devices managedVSAvoidnetwork administration workload

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system enables automated self-service through machine learning models that automatically detect anomalies, classify device behaviors, and generate remediation recommendations without human intervention. The platform autonomously monitors network traffic, identifies compromised devices, and presents remediation options to administrators, significantly reducing manual workload while managing large-scale IoT deployments

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual network administration tasks are replaced by automated machine learning-based anomaly detection systems. The system substitutes human analysts with AI models that continuously analyze network traffic patterns, device behaviors, and security events to identify and remediate threats automatically, scaling management capabilities without proportionally increasing administrative overhead

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual investigation and diagnosis methods are used for each anomalous device, then detailed analysis can be performed, but the time and labor required becomes prohibitively high for large-scale networks

Engineering Contradiction:

Improveanomaly detection accuracyVSAvoidtime for device investigation and remediation

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary automated investigation and diagnosis before administrator intervention. Machine learning models pre-analyze network traffic, device metadata, and behavior patterns to generate comprehensive anomaly reports and remediation recommendations in advance, so when administrators review cases, the heavy lifting of detection and initial analysis has already been completed

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates detailed digital copies and representations of anomalous device behaviors through machine learning analysis. Instead of manually examining each device, the system generates replicated anomaly profiles, traffic pattern copies, and behavior signatures that capture essential diagnostic information, allowing rapid review and comparison across multiple devices without repeating full investigation procedures

Inventive Principle:

Principle #26Copying

3Reliability

If comprehensive monitoring of all IoT devices is implemented, then security breaches can be detected, but the complexity of managing heterogeneous devices across multiple network layers increases

Engineering Contradiction:

Improvenetwork securityVSAvoidsystem complexity for managing heterogeneous devices

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements a universal machine learning-based monitoring framework that handles diverse IoT device types, protocols, and network layers through a single unified platform. The anomaly detection models are designed to work across multiple transport layers (L4-L7) and accommodate various device heterogeneities, providing comprehensive security monitoring without requiring separate specialized systems for each device category

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces machine learning models as intermediary layers between raw network traffic and administrator decision-making. These intermediary AI components translate complex, heterogeneous device behaviors into standardized anomaly scores and classifications, simplifying the management of diverse IoT devices by mediating between底层 complexity and upper-layer security policies

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11102236B2Systems and methods for remediating internet of things devices

Publication Date: 2021.08.24 CISCO TECHNOLOGY INC

AI summary

Systems and methods provide for identification and remediation of IoT devices exhibiting anomalous behaviors. An IoT management system can identify IoT devices requiring remediation. The IoT management system may present a first interface including representations of the devices requiring remediation, where each representation can include identifying information for an IoT device, policies applied to the IoT device, and bandwidth/throughput information of the IoT device. The IoT management system can present a second remediation interface representing a detailed representation of a first IoT device. The detailed representation can include user interface elements representing actions to be performed relating to the first IoT device. The IoT management system can perform a first action corresponding to a selection of one of the user interface elements.