Endpoint Relocation Detection Using Network Environment Hashing
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Solution Overview
Problem
Existing network security systems struggle to detect unauthorized relocation of endpoint devices in dynamic and distributed network environments, leading to security risks such as data exposure, bypassing of security controls, asset tracking difficulties, and increased vulnerability to insider threats, with current solutions being time-consuming, error-prone, and lacking real-time detection capabilities.
Innovation Solution
A computer-implemented system comprising network security appliances and servers that collect and analyze network environment data using LLDP, MAC addresses, and other protocols to generate Paranoia-Diff-Scores (PDS) for detecting unauthorized relocation, employing machine learning to assess risk levels and trigger alerts or cease network activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static IP address mapping or manual audits are used to detect unauthorized relocation, then the system complexity is reduced, but the detection precision and real-time capability deteriorate
Solution Approach 1:
The system segments the detection process into multiple independent modules: data collection modules (DHCP, LLDP, WLAN), feature extraction modules, risk calculation modules, and alert generation modules. Each module handles specific aspects of detection, allowing the system to achieve high detection precision through specialized processing while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by collecting network environment data continuously in advance (DHCP options, LLDP neighbors, WLAN information) and pre-processing features before unauthorized relocation occurs. This allows the system to detect relocations in real-time without requiring complex analysis at the moment of detection, thereby improving detection precision while reducing computational complexity during critical moments.
2Speed
If real-time detection capabilities are implemented, then the response time to unauthorized relocation is improved, but the device complexity and computational resources required worsen
Solution Approach 1:
The system employs periodic action by continuously collecting network data at regular intervals (DHCP transactions, LLDP advertisements, WLAN beacons) rather than performing complex analysis continuously. This allows real-time detection capability through periodic data gathering followed by immediate feature extraction and risk assessment, achieving fast response without requiring constant heavy computational processing.
Solution Approach 2:
The system extracts only the essential features from the collected network data (MAC addresses, IP addresses, network topology information) for risk assessment, separating the bulk data storage from the critical analysis process. This extraction approach enables real-time detection by working with condensed feature representations rather than raw data, reducing computational complexity while maintaining detection speed.
3Measurement precision
If comprehensive network environment data collection is performed, then the detection accuracy is improved, but the data processing complexity and time consumption worsen
Solution Approach 1:
The system extracts and processes only the most relevant features from comprehensive network data: MAC addresses for device identification, IP addresses for location mapping, LLDP neighbors for topology information, and WLAN SSIDs for network context. By focusing analysis on these key features rather than processing all raw data, the system achieves high detection accuracy while minimizing processing time.
Solution Approach 2:
The system implements partial action by collecting comprehensive data (excessive action) through multiple protocols but then applying selective processing (partial action) by analyzing only the features necessary for relocation detection. This approach ensures detection accuracy through comprehensive data collection while reducing processing time through selective feature analysis rather than processing all collected data.
4Reliability
If adaptive risk assessment with multiple severity levels is implemented, then the false positive rate is reduced, but the device complexity and processing requirements worsen
Solution Approach 1:
The system applies local quality by assigning different risk thresholds and assessment criteria to different network environments and device types. Instead of using a single uniform risk assessment model, the system adapts its evaluation parameters based on the specific network context, device importance, and historical behavior patterns. This localized approach reduces false positives by tailoring assessment strictness to local conditions while managing complexity through context-aware processing.
Solution Approach 2:
The system implements dynamics by making the risk assessment thresholds and detection sensitivity adjustable based on current network conditions, historical data, and security priorities. The system can dynamically raise or lower alert thresholds depending on the operational context, allowing adaptive risk assessment that reduces false positives without requiring complex static rule sets. This dynamic adjustment simplifies the system architecture compared to maintaining multiple fixed assessment models.
Data Source
AI summary
A system and method for detecting unauthorized relocation of endpoint devices are disclosed. The system comprises one or more network security appliances and one or more servers. The one or more network security appliances is configured to collect network environment data associated with each endpoint device of the one or more endpoint devices. The one or more servers is configured to obtain the network environment data from the one or more network security appliances for generating one or more hash values for the network environment data of each endpoint device of the one or more endpoint devices. The system performs a comparative analysis between the one or more hash values to generate a Paranoia-Diff-Score (PDS) for each endpoint device of the one or more endpoint devices to detect the unauthorized relocation of the one or more endpoint devices.