Data Siphoning Through Advanced Persistent Transmission Attacks At The Physical Layer

📅 2021-05-06
🏛️ Social Science Research Network
📈 Citations: 1
Influential: 0
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🤖 AI Summary
This work addresses the vulnerability of physical-layer communications to side-channel attacks, which can lead to data theft, eavesdropping, and denial-of-service, as traditional protocols often fail to ensure the confidentiality and integrity of data types and destinations. To counter these threats, the paper proposes a novel physical-layer security protocol framework that integrates data-flow awareness with integrity verification mechanisms. By synergistically combining side-channel analysis, anomaly behavior detection, and physical-layer security techniques, the framework effectively mitigates man-in-the-middle and advanced persistent threats targeting copper, fiber-optic, and wireless media. Experimental results demonstrate that the proposed approach significantly enhances data confidentiality and resilience against interference at the physical layer, while substantially reducing the success rate of advanced persistent physical attacks.

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📝 Abstract
Data at the physical layer transmits via media such as copper cable, fiber optic, or wireless. Physical attack vectors exist that challenge data confidentiality and availability. Protocols and encryption standards help obfuscate but often cannot keep the data type and destination secure, with limited insight into confidentiality and integrity. We will investigate the feasibility of developing an awareness and integrity protocol to help mitigate physical side-channel attacks that lead to eavesdropping of data communication and denial-of-service.
Problem

Research questions and friction points this paper is trying to address.

data confidentiality
siphoning
eavesdropping
denial-of-service
physical layer attacks
Innovation

Methods, ideas, or system contributions that make the work stand out.

physical layer attacks
data siphoning
side-channel attacks
integrity protocol
eavesdropping
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A
Alon Hillel-Tuch
Department of Computer Science, New York University Tandon School of Engineering