Network Anomaly Detection via Dynamic Behavioral Indicators
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As the number of network-connected devices increases, especially with the advancement of IoT, networks become more vulnerable to security attacks due to the lack of hardware and software capabilities in IoT devices, and static security rules fail to effectively identify anomalies.
Innovation Solution
Implementing a method in network nodes with processors and memory to generate characteristic indicators for device types based on communications, determine performance indicators, and synthesize anomaly indicators to detect and prevent security threats by grouping devices, generating characteristic indicators for device types, determining performance indicators, and synthesizing anomaly indicators based on these indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static security rules are used for network protection, then the security system is simple to implement, but it fails to identify anomalies in networks with diverse device types
Solution Approach 1:
The patent implements dynamic anomaly detection by continuously learning device behavior patterns and adapting security rules based on observed communications. The system transitions from static predefined rules to dynamic rules that evolve with network conditions, enabling reliable anomaly identification while managing complexity through automated adaptation.
Solution Approach 2:
The system changes security parameters dynamically by adjusting detection thresholds, rule weights, and analysis depths based on network context. This allows the security system to maintain high reliability across diverse device types while managing computational complexity through adaptive parameter adjustment rather than fixed complex rules.
2Adaptability or versatility
If the number of network-connected devices increases, then network coverage and functionality improve, but network vulnerability to security attacks increases
Solution Approach 1:
The patent implements self-service security where each device learns its own normal behavior patterns and contributes to collective anomaly detection. Devices autonomously monitor their own communications and help identify threats in the network, enabling security protection that scales with device count without proportionally increasing central system complexity or vulnerability.
Solution Approach 2:
The system uses feedback mechanisms where detected anomalies and attack patterns are fed back into the learning model to improve future detection. This continuous feedback loop enables the network to adapt to new threat types as devices are added, maintaining security effectiveness while supporting growing network diversity.
3Adaptability or versatility
If IoT devices are deployed to expand network functionality, then network versatility improves, but hardware and software capability to prevent security attacks decreases
Solution Approach 1:
The patent introduces an intermediary behavioral analysis layer that sits between IoT devices and the network. This intermediary monitors device communications and identifies anomalies without requiring direct security capabilities in the IoT devices themselves, enabling versatile device deployment while maintaining security through external behavioral analysis.
Solution Approach 2:
The system replaces traditional hardware-based security mechanisms (firewalls, intrusion prevention systems) with software-based behavioral analysis. This substitution allows IoT devices with limited hardware capabilities to be secured through cloud-based or network-based behavioral monitoring that analyzes communication patterns rather than requiring strong local security hardware.
Data Source
AI summary
Various implementations disclosed herein enable identifying anomalies in a network. For example, in various implementations, a method of identifying anomalies in a network is performed by a network node. In various implementations, the network node includes one or more processors, and a non-transitory memory. In various implementations, the method includes generating a characteristic indicator that characterizes a device type based on communications associated with a first device of the device type. In various implementations, the method includes determining, based on communications associated with the first device, a performance indicator that indicates a performance of the first device. In various implementations, the method includes synthesizing an anomaly indicator as a function of the performance indicator in relation to the characteristic indicator.