Abstract:
Identity-based attacks (IBAs) are a major wireless network threat. RSS is becoming more popular for wireless network IBA detection. Current mobile schemes generate too many false alarms.
This project proposes a stronger Reciprocal Channel Variation-based Identification and classification (RCVIC) scheme for mobile wireless networks that exploits the reciprocity of the wireless fading channel and RSS variations caused by mobility to improve detection performance.
RCVIC detects IBAs in multiple stages, unlike current schemes. RCVIC divides frames into two classes if IBAs are detected. Network forensics, attacker localization, trajectory analysis, and other analyses benefit from frames from the same senders.
Theoretical analysis and simulations assess RCVIC feasibility. Experiments with off-the-shelf 802.11 devices under different attacking patterns in real indoor and outdoor mobile scenarios validate it.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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