Distributed Wireless Security Filters for Malicious Activity Detection
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Solution Overview
Problem
The increasing number of wireless communicating devices in IoT and smart systems poses a higher vulnerability to cyber attacks due to the ease of intercepting and manipulating wireless communications, making it challenging to detect and protect against malicious activity effectively.
Innovation Solution
A distributed system involving multiple 'Protect' devices equipped with sensors to observe wireless communications, a central control server (C3) that provides filters to distinguish between normal and malicious activity, and device profiles to identify atypical behavior, enabling efficient detection and reporting of malicious activity even in resource-constrained environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless communications are used to enable flexibility and mobility of devices, then ease of operation and adaptability are improved, but vulnerability to cyber attacks and malicious activity increases
Solution Approach 1:
The system performs preliminary actions by deploying multiple Protect devices to continuously monitor and sniff wireless communications before attacks can succeed. The C3 server pre-processes captured data to create filters and device profiles that enable early detection of malicious activity patterns, allowing the system to prepare defenses in advance rather than reacting after attacks occur.
Solution Approach 2:
The C3 server acts as an intermediary between the distributed Protect devices and the analysis processing. It receives captured wireless communication data from multiple Protects, performs centralized analysis to generate filters and device profiles, then distributes these back to the Protects. This intermediary architecture enables sophisticated security analysis without requiring complex processing at each individualProtect device.
2Measurement precision
If multiple Protect devices are deployed to detect malicious activity, then detection capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the security monitoring function into distributed Protect devices that each perform localized wireless communication sniffing and basic filtering. EachProtect device operates independently to capture data in its vicinity, then segments the analysis task by sending only relevant captured data to the C3 server for centralized filter generation. This segmentation allows detection capability to scale with the number ofProtect devices without proportionally increasing individual device complexity.
Solution Approach 2:
The C3 server provides universal functionality by serving multipleProtect devices with a single centralized analysis engine. It generates filters and device profiles that are then universally applied across all connectedProtect devices, enabling the same sophisticated detection capabilities to be shared by multiple devices without each needing independent complex processing hardware.
3Measurement precision
If filters and device profiles are used to distinguish benign from malicious activity, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The C3 server performs preliminary action by pre-processing captured wireless communication data to generate filters and device profiles before actual detection is needed. These pre-generated filters contain extracted features and patterns that enable rapid matching during runtime, allowing the system to invest processing time upfront in filter creation rather than during real-time detection operations.
Solution Approach 2:
The system extracts only the essential features and patterns from captured wireless communication data to create compact filters and device profiles. Instead of analyzing entire data streams in real-time, the C3 server extracts key characteristics (such as device identifiers, communication patterns, and behavioral features) and stores them in condensed filter representations that can be quickly matched against new traffic, significantly reducing processing time while maintaining detection accuracy.
Data Source
AI summary
Distributed techniques for detecting atypical or malicious wireless communications activity are disclosed. A server can iteratively generate sets of filters based at least in part upon observation data received from one or more Protects. The filters can be used by the Protect(s) to distinguish between sniffed wireless messages that are to be discarded and those that are to be reported to the server. The server can provide the generated sets of filters to the Protect(s) to cause the Protect(s) to process additional sniffed wireless messages utilizing the one or more sets of filters. Updated filters can cause fewer subsequent sniffed wireless messages to be reported than would have been reported by use of previous filters. Limited activity reporting by the Protect(s) enables a reduced communication load compared to full activity reporting without degrading the ability of the server to detect the atypical or malicious wireless communications activity.