Distributed IoT Intrusion Detection With Centralized Event Correlation
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Solution Overview
Problem
Existing intrusion detection systems (IDS) for IoT devices face challenges due to their limited processing power, memory, and network capabilities, making it difficult to implement effective security measures directly on these devices.
Innovation Solution
A distributed intrusion detection system (IDS) comprising a plurality of sensors in IoT devices and a server that collects and analyzes event data through a network, utilizing inspection and reconnaissance engines to monitor system health and network activity, and correlates data to detect intrusion events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intrusion detection systems are implemented directly on IoT devices, then security detection capability is improved, but device processing power and energy consumption are insufficient
Solution Approach 1:
The intrusion detection system is segmented into distributed sensors deployed on IoT devices and a centralized server for heavy processing. Sensors collect and transmit event data to the server, which performs correlation analysis and generates alerts. This segmentation allows lightweight device implementation while maintaining comprehensive detection capability.
Solution Approach 2:
A centralized server acts as an intermediary between distributed IDS sensors and the analysis processing. The server receives event data from multiple sensors, performs correlation analysis, and generates alerts. This intermediary approach enables complex analysis without burdening individual IoT devices with high processing requirements.
2Measurement precision
If more sensors and analysis capabilities are added to IoT devices, then intrusion detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The system segments detection functions into simple sensors on devices and complex analysis on a centralized server. Sensors perform basic event collection while the server handles correlation analysis across multiple data sources. This segmentation achieves high detection accuracy without increasing individual device complexity.
Solution Approach 2:
Multiple IDS sensors from different IoT devices are merged into a unified analysis system on the server. The server correlates event data from multiple sensors to detect intrusion patterns that individual sensors cannot identify alone. This merging approach improves detection accuracy while keeping individual device implementations simple.
Data Source
AI summary
A distributed intrusion detection system (IDS) is provided. The IDS includes, but is not limited to: a plurality of IDS sensors distributed in internet of things (IoT) devices; and a server, coupled to the plurality of IDS sensors through a network, where the plurality of IDS sensors is configured to collect event data from the IoT devices, and the server is configured to: receive the collected event data from the plurality of IDS sensors; determine if an intrusion event occurs by correlating and analyzing the collected event data; and generate an alert in an instance in which the intrusion event occurs.