Ambient IoT Attacker Detection Using Trick Tag Power Profiling
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Solution Overview
Problem
Existing IoT devices are vulnerable to fraudulent devices that emit malicious or jamming signals, compromising the detection integrity in cellular-based deployments, making it challenging to distinguish malicious behaviors from legitimate bad link conditions.
Innovation Solution
An NR network element acts as a trick tag (TT) to stimulate attackers, while another element monitors the spectrum for pollution, profiling attackers based on power profiles during specific periods, and transmitting this information to a network device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fraudulent devices emit malicious or jamming signals to block passive tag signals, then the detection integrity is compromised, but the ability to distinguish malicious behaviors from legitimate bad link conditions deteriorates
Solution Approach 1:
The system performs preliminary actions by establishing baseline power profiles during quiet periods before the actual detection session. These pre-characterized baseline profiles represent normal operational conditions and are used later to identify deviations caused by jamming attacks, enabling the system to distinguish malicious behaviors from legitimate bad link conditions.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring received power levels during quiet periods and comparing them against established baseline profiles. When deviations exceed threshold values, the system generates feedback signals to trigger attacker detection procedures, allowing dynamic adaptation to changing detection environments.
2Measurement precision
If the system monitors power profiles during quiet periods to detect attackers, then the accuracy of attacker detection is improved, but the system complexity increases
Solution Approach 1:
The detection session is segmented into distinct temporal phases: quiet periods for baseline profiling, active periods for data collection, and analysis periods for attacker detection. This segmentation allows the system to focus computational resources on specific tasks at specific times, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The system applies partial action by monitoring power profiles only during quiet periods when no legitimate tag signals are transmitted, rather than continuously analyzing all received signals. This selective monitoring approach reduces processing requirements while maintaining accurate attacker detection capability.
Data Source
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Figure 3A~3B
AI summary
Example embodiments of the present disclosure relate to attacker detection in ambient IoT. An apparatus receives at least one signal which is at least associated with a trick tag signal transmitted from a terminal device. The apparatus determines a number of attackers of a tag detection session based on the at least one signal. The apparatus transmits, to a network device, attacker information at least comprising the number of attackers. In this way, the NR element is enabled to identify and report jamming activities to the network in time. Therefore, the tag detection integrity can be guaranteed.